1 code implementation • 24 May 2023 • Shishir G. Patil, Tianjun Zhang, Xin Wang, Joseph E. Gonzalez
Large Language Models (LLMs) have seen an impressive wave of advances recently, with models now excelling in a variety of tasks, such as mathematical reasoning and program synthesis.
1 code implementation • 15 Jun 2022 • Sheng Zhou, Hongjia Xu, Zhuonan Zheng, Jiawei Chen, Zhao Li, Jiajun Bu, Jia Wu, Xin Wang, Wenwu Zhu, Martin Ester
Motivated by the tremendous success of deep learning in clustering, one of the most fundamental machine learning tasks, and the large number of recent advances in this direction, in this paper we conduct a comprehensive survey on deep clustering by proposing a new taxonomy of different state-of-the-art approaches.
2 code implementations • 1 Mar 2021 • Ziwei Zhang, Xin Wang, Wenwu Zhu
Machine learning on graphs has been extensively studied in both academic and industry.
2 code implementations • ICLR Workshop GTRL 2021 • Ziwei Zhang, Yijian Qin, Zeyang Zhang, Chaoyu Guan, Jie Cai, Heng Chang, Jiyan Jiang, Haoyang Li, Zixin Sun, Beini Xie, Yang Yao, YiPeng Zhang, Xin Wang, Wenwu Zhu
To fill this gap, we present Automated Graph Learning (AutoGL), the first dedicated library for automated machine learning on graphs.
5 code implementations • ICML 2020 • Xin Wang, Thomas E. Huang, Trevor Darrell, Joseph E. Gonzalez, Fisher Yu
Such a simple approach outperforms the meta-learning methods by roughly 2~20 points on current benchmarks and sometimes even doubles the accuracy of the prior methods.
Ranked #17 on Few-Shot Object Detection on MS-COCO (30-shot)
2 code implementations • 31 Jan 2019 • Sheng Zhou, Jiajun Bu, Xin Wang, Jia-Wei Chen, Can Wang
Second, given a meta path, nodes in HIN are connected by path instances while existing works fail to fully explore the differences between path instances that reflect nodes' preferences in the semantic space.
6 code implementations • 17 Apr 2019 • Chen-Chou Lo, Szu-Wei Fu, Wen-Chin Huang, Xin Wang, Junichi Yamagishi, Yu Tsao, Hsin-Min Wang
In this paper, we propose deep learning-based assessment models to predict human ratings of converted speech.
3 code implementations • 6 Dec 2023 • Zhongwei Wan, Xin Wang, Che Liu, Samiul Alam, Yu Zheng, Jiachen Liu, Zhongnan Qu, Shen Yan, Yi Zhu, Quanlu Zhang, Mosharaf Chowdhury, Mi Zhang
Large Language Models (LLMs) have demonstrated remarkable capabilities in important tasks such as natural language understanding, language generation, and complex reasoning and have the potential to make a substantial impact on our society.
1 code implementation • 18 May 2020 • Youle Wang, Guangxi Li, Xin Wang
By performing numerical experiments, we show that shallow parameterized circuits with only one additional qubit can be trained to prepare the Ising chain and spin chain Gibbs states with a fidelity higher than 95%.
1 code implementation • 3 Jun 2020 • Xin Wang, Zhixin Song, Youle Wang
In this work, we propose a variational quantum algorithm for singular value decomposition (VQSVD).
1 code implementation • 10 Dec 2020 • Ranyiliu Chen, Zhixin Song, Xuanqiang Zhao, Xin Wang
A novel variational algorithm for trace distance estimation is then derived from this technique, with the assistance of a single ancillary qubit.
Quantum Physics Information Theory Mathematical Physics Information Theory Mathematical Physics Optimization and Control
1 code implementation • 15 Dec 2020 • Chenfeng Cao, Xin Wang
Based on this understanding, we present a noise-assisted quantum autoencoder algorithm to go beyond the limitations, our model can achieve high recovering fidelity for general input states.
Quantum Physics
1 code implementation • 15 Dec 2020 • Guangxi Li, Zhixin Song, Xin Wang
Classification of quantum data is essential for quantum machine learning and near-term quantum technologies.
1 code implementation • 28 Dec 2020 • Kun Wang, Zhixin Song, Xuanqiang Zhao, Zihe Wang, Xin Wang
Firstly, it decomposes a positive map into a combination of quantum operations implementable on near-term quantum devices.
Quantum Physics Strongly Correlated Electrons
2 code implementations • 28 Jan 2021 • Xuanqiang Zhao, Benchi Zhao, Zihe Wang, Zhixin Song, Xin Wang
Here we introduce LOCCNet, a machine learning framework facilitating protocol design and optimization for distributed quantum information processing tasks.
4 code implementations • ICCV 2019 • Bingyi Kang, Zhuang Liu, Xin Wang, Fisher Yu, Jiashi Feng, Trevor Darrell
The feature learner extracts meta features that are generalizable to detect novel object classes, using training data from base classes with sufficient samples.
Ranked #21 on Few-Shot Object Detection on MS-COCO (30-shot)
1 code implementation • ECCV 2018 • Xin Wang, Wenhan Xiong, Hongmin Wang, William Yang Wang
In this paper, we take a radical approach to bridge the gap between synthetic studies and real-world practices---We propose a novel, planned-ahead hybrid reinforcement learning model that combines model-free and model-based reinforcement learning to solve a real-world vision-language navigation task.
Model-based Reinforcement Learning reinforcement-learning +4
3 code implementations • CVPR 2020 • Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, Trevor Darrell
Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving.
Ranked #5 on Multiple Object Tracking on BDD100K test
1 code implementation • CVPR 2022 • Amir Bar, Xin Wang, Vadim Kantorov, Colorado J Reed, Roei Herzig, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson
Recent self-supervised pretraining methods for object detection largely focus on pretraining the backbone of the object detector, neglecting key parts of detection architecture.
Ranked #1 on Few-Shot Object Detection on COCO 2017
1 code implementation • 10 Oct 2021 • Xin Wang, Junichi Yamagishi
On the ASVspoof2019 logical access database, the results demonstrate that an energy-based estimator and a neural-network-based one achieved acceptable performance in identifying unknown attacks in the test set.
1 code implementation • 10 Jan 2022 • Xin Wang, Junichi Yamagishi
Presentation attack detection (PAD) for ASV, or speech anti-spoofing, is therefore indispensable.
1 code implementation • 28 Mar 2022 • Xin Wang, Junich Yamagishi
This study took the initiative and investigated CM training using active learning (AL), a framework that iteratively selects useful data from a large pool set and fine-tunes the CM.
1 code implementation • 19 Oct 2022 • Xin Wang, Junichi Yamagishi
To make better use of pairs of bona fide and spoofed data, this study introduces a contrastive feature loss that can be plugged into the standard training criterion.
1 code implementation • 12 Sep 2023 • Xin Wang, Junichi Yamagishi
While many datasets use spoofed data generated by speech synthesis systems, it was recently found that data vocoded by neural vocoders were also effective as the spoofed training data.
3 code implementations • 23 Oct 2019 • Erica Cooper, Cheng-I Lai, Yusuke Yasuda, Fuming Fang, Xin Wang, Nanxin Chen, Junichi Yamagishi
While speaker adaptation for end-to-end speech synthesis using speaker embeddings can produce good speaker similarity for speakers seen during training, there remains a gap for zero-shot adaptation to unseen speakers.
Audio and Speech Processing
1 code implementation • 14 Nov 2023 • Yitao Zhu, Zhenrong Shen, Zihao Zhao, Sheng Wang, Xin Wang, Xiangyu Zhao, Dinggang Shen, Qian Wang
By fixing the weight of ViT models and only adding small low-rank plug-ins, we achieve competitive results on various diagnosis tasks across different imaging modalities using only a few trainable parameters.
2 code implementations • ECCV 2018 • Xin Wang, Fisher Yu, Zi-Yi Dou, Trevor Darrell, Joseph E. Gonzalez
While deeper convolutional networks are needed to achieve maximum accuracy in visual perception tasks, for many inputs shallower networks are sufficient.
1 code implementation • 28 Aug 2023 • Ziwei Zhang, Haoyang Li, Zeyang Zhang, Yijian Qin, Xin Wang, Wenwu Zhu
In order to promote applying large models for graphs forward, we present a perspective paper to discuss the challenges and opportunities associated with developing large graph models.
1 code implementation • 27 Apr 2023 • Joo Hyung Lee, Wonpyo Park, Nicole Mitchell, Jonathan Pilault, Johan Obando-Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart Bik, Woohyun Han, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann Dauphin, Gintare Karolina Dziugaite, Pablo Samuel Castro, Utku Evci
This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research.
2 code implementations • 24 Jan 2020 • Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat, Maximilian Löffler, Hans Liebl, Hongwei Li, Giles Tetteh, Jan Kukačka, Christian Payer, Darko Štern, Martin Urschler, Maodong Chen, Dalong Cheng, Nikolas Lessmann, Yujin Hu, Tianfu Wang, Dong Yang, Daguang Xu, Felix Ambellan, Tamaz Amiranashvili, Moritz Ehlke, Hans Lamecker, Sebastian Lehnert, Marilia Lirio, Nicolás Pérez de Olaguer, Heiko Ramm, Manish Sahu, Alexander Tack, Stefan Zachow, Tao Jiang, Xinjun Ma, Christoph Angerman, Xin Wang, Kevin Brown, Alexandre Kirszenberg, Élodie Puybareau, Di Chen, Yiwei Bai, Brandon H. Rapazzo, Timyoas Yeah, Amber Zhang, Shangliang Xu, Feng Hou, Zhiqiang He, Chan Zeng, Zheng Xiangshang, Xu Liming, Tucker J. Netherton, Raymond P. Mumme, Laurence E. Court, Zixun Huang, Chenhang He, Li-Wen Wang, Sai Ho Ling, Lê Duy Huynh, Nicolas Boutry, Roman Jakubicek, Jiri Chmelik, Supriti Mulay, Mohanasankar Sivaprakasam, Johannes C. Paetzold, Suprosanna Shit, Ivan Ezhov, Benedikt Wiestler, Ben Glocker, Alexander Valentinitsch, Markus Rempfler, Björn H. Menze, Jan S. Kirschke
Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf. io/nqjyw/, https://osf. io/t98fz/).
1 code implementation • 24 May 2023 • Baifeng Shi, Siyu Gai, Trevor Darrell, Xin Wang
We introduce Top-Down Attention Steering (TOAST), a novel transfer learning algorithm that keeps the pre-trained backbone frozen, selects task-relevant features in the output, and feeds those features back to the model to steer the attention to the task-specific features.
1 code implementation • 16 Apr 2023 • Hanlei Zhang, Hua Xu, Xin Wang, Fei Long, Kai Gao
New intent discovery is of great value to natural language processing, allowing for a better understanding of user needs and providing friendly services.
1 code implementation • Radiology 2020 • Lin Li, Lixin Qin, Zeguo Xu, Youbing Yin, Xin Wang, Bin Kong, Junjie Bai, Yi Lu, Zhenghan Fang, Qi Song, Kunlin Cao, Daliang Liu, Guisheng Wang, Qizhong Xu, Xisheng Fang, Shiqin Zhang, Juan Xia, Jun Xia
Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19.
1 code implementation • 1 Sep 2021 • Héctor Delgado, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, Xuechen Liu, Andreas Nautsch, Jose Patino, Md Sahidullah, Massimiliano Todisco, Xin Wang, Junichi Yamagishi
The automatic speaker verification spoofing and countermeasures (ASVspoof) challenge series is a community-led initiative which aims to promote the consideration of spoofing and the development of countermeasures.
1 code implementation • 15 Nov 2021 • Xin Wang, Junichi Yamagishi
Self-supervised speech model is a rapid progressing research topic, and many pre-trained models have been released and used in various down stream tasks.
1 code implementation • 22 Jul 2022 • Yunhao Ge, Harkirat Behl, Jiashu Xu, Suriya Gunasekar, Neel Joshi, Yale Song, Xin Wang, Laurent Itti, Vibhav Vineet
However, existing approaches either require human experts to manually tune each scene property or use automatic methods that provide little to no control; this requires rendering large amounts of random data variations, which is slow and is often suboptimal for the target domain.
1 code implementation • CVPR 2023 • Baifeng Shi, Trevor Darrell, Xin Wang
In this paper, we consider top-down attention from a classic Analysis-by-Synthesis (AbS) perspective of vision.
1 code implementation • 19 Mar 2024 • Baifeng Shi, Ziyang Wu, Maolin Mao, Xin Wang, Trevor Darrell
Our results show that a multi-scale smaller model has comparable learning capacity to a larger model, and pre-training smaller models with S$^2$ can match or even exceed the advantage of larger models.
2 code implementations • ACL 2018 • Xin Wang, Wenhu Chen, Yuan-Fang Wang, William Yang Wang
Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem.
Ranked #13 on Visual Storytelling on VIST
1 code implementation • CVPR 2022 • Xuefeng Du, Xin Wang, Gabriel Gozum, Yixuan Li
Building reliable object detectors that can detect out-of-distribution (OOD) objects is critical yet underexplored.
1 code implementation • 29 Oct 2018 • Yusuke Yasuda, Xin Wang, Shinji Takaki, Junichi Yamagishi
Towards end-to-end Japanese speech synthesis, we extend Tacotron to systems with self-attention to capture long-term dependencies related to pitch accents and compare their audio quality with classical pipeline systems under various conditions to show their pros and cons.
1 code implementation • CVPR 2020 • Yuankai Qi, Qi Wu, Peter Anderson, Xin Wang, William Yang Wang, Chunhua Shen, Anton Van Den Hengel
One of the long-term challenges of robotics is to enable robots to interact with humans in the visual world via natural language, as humans are visual animals that communicate through language.
2 code implementations • 23 Jan 2022 • Xin Wang, Serdar Kadioglu
We introduce a pattern mining framework that operates on semi-structured datasets and exploits the dichotomy between outcomes.
2 code implementations • 22 Nov 2021 • Bernard Kleynhans, Xin Wang, Serdar Kadıoğlu
Designing recommendation systems with limited or no available training data remains a challenge.
1 code implementation • ICCV 2021 • Zeyu Hu, Xuyang Bai, Jiaxiang Shang, Runze Zhang, Jiayu Dong, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai
Experimental results validate the effectiveness of VMNet: specifically, on the challenging ScanNet dataset for large-scale segmentation of indoor scenes, it outperforms the state-of-the-art SparseConvNet and MinkowskiNet (74. 6% vs 72. 5% and 73. 6% in mIoU) with a simpler network structure (17M vs 30M and 38M parameters).
1 code implementation • 19 Oct 2020 • Long Chen, Feixiang Zhou, Shengke Wang, Junyu Dong, Ning li, Haiping Ma, Xin Wang, Huiyu Zhou
Moreover, inspired by the human education process that drives the learning from easy to hard concepts, we here propose the CMA training paradigm that first trains a clean detector which is free from the influence of noisy data.
1 code implementation • 4 Jan 2022 • Xin Wang, Ziwei Zhang, Wenwu Zhu
Graph machine learning has been extensively studied in both academic and industry.
1 code implementation • 30 Nov 2023 • Bin Huang, Xin Wang, Hong Chen, Zihan Song, Wenwu Zhu
Large language models (LLMs) have shown remarkable text understanding capabilities, which have been extended as Video LLMs to handle video data for comprehending visual details.
Dense Video Captioning Video-based Generative Performance Benchmarking (Consistency) +5
1 code implementation • arXiv 2024 • Kai Xu, Ziwei Yu, Xin Wang, Michael Bi Mi, Angela Yao
We show that bilinear interpolation inherently attenuates high-frequency information while an MLP-based coordinate network can approximate more frequencies.
Ranked #1 on Video Super-Resolution on Vid4 - 4x upscaling
1 code implementation • 2 Aug 2021 • Tianlong Kong, Shouyi Yin, Dawei Zhang, Wang Geng, Xin Wang, Dandan song, Jinwen Huang, Huiyu Shi, Xiaorui Wang
To address this issue, we propose a new architecture, named dynamic multi-scale convolution, which consists of dynamic kernel convolution, local multi-scale learning, and global multi-scale pooling.
2 code implementations • ECCV 2020 • Xuewen Yang, Heming Zhang, Di Jin, Yingru Liu, Chi-Hao Wu, Jianchao Tan, Dongliang Xie, Jue Wang, Xin Wang
The goal of this work is to develop a novel learning framework for accurate and expressive fashion captioning.
1 code implementation • 26 Jul 2019 • Xin Wang, Bo Wu, Yun Ye, Yueqi Zhong
Existing works about fashion outfit compatibility focus on predicting the overall compatibility of a set of fashion items with their information from different modalities.
1 code implementation • CVPR 2022 • Ching-Yao Chuang, R Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, Yale Song
Contrastive learning relies on an assumption that positive pairs contain related views, e. g., patches of an image or co-occurring multimodal signals of a video, that share certain underlying information about an instance.
1 code implementation • ICCV 2021 • Xin Wang, Thomas E. Huang, Benlin Liu, Fisher Yu, Xiaolong Wang, Joseph E. Gonzalez, Trevor Darrell
Building reliable object detectors that are robust to domain shifts, such as various changes in context, viewpoint, and object appearances, is critical for real-world applications.
1 code implementation • 24 Feb 2022 • Hemlata Tak, Massimiliano Todisco, Xin Wang, Jee-weon Jung, Junichi Yamagishi, Nicholas Evans
The performance of spoofing countermeasure systems depends fundamentally upon the use of sufficiently representative training data.
1 code implementation • 23 Mar 2022 • Natalia Tomashenko, Xin Wang, Xiaoxiao Miao, Hubert Nourtel, Pierre Champion, Massimiliano Todisco, Emmanuel Vincent, Nicholas Evans, Junichi Yamagishi, Jean-François Bonastre
Participants apply their developed anonymization systems, run evaluation scripts and submit objective evaluation results and anonymized speech data to the organizers.
1 code implementation • 9 Sep 2022 • Hanlei Zhang, Hua Xu, Xin Wang, Qianrui Zhou, Shaojie Zhao, Jiayan Teng
This paper introduces a novel dataset for multimodal intent recognition (MIntRec) to address this issue.
Ranked #1 on Multimodal Intent Recognition on MIntRec
3 code implementations • 4 May 2020 • Natalia Tomashenko, Brij Mohan Lal Srivastava, Xin Wang, Emmanuel Vincent, Andreas Nautsch, Junichi Yamagishi, Nicholas Evans, Jose Patino, Jean-François Bonastre, Paul-Gauthier Noé, Massimiliano Todisco
The VoicePrivacy initiative aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the evaluation methodology, and benchmarking solutions through a series of challenges.
1 code implementation • 1 Sep 2021 • Natalia Tomashenko, Xin Wang, Emmanuel Vincent, Jose Patino, Brij Mohan Lal Srivastava, Paul-Gauthier Noé, Andreas Nautsch, Nicholas Evans, Junichi Yamagishi, Benjamin O'Brien, Anaïs Chanclu, Jean-François Bonastre, Massimiliano Todisco, Mohamed Maouche
We provide a systematic overview of the challenge design with an analysis of submitted systems and evaluation results.
1 code implementation • 14 May 2022 • Natalia Tomashenko, Brij Mohan Lal Srivastava, Xin Wang, Emmanuel Vincent, Andreas Nautsch, Junichi Yamagishi, Nicholas Evans, Jose Patino, Jean-François Bonastre, Paul-Gauthier Noé, Massimiliano Todisco
The VoicePrivacy Challenge aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the evaluation methodology, and benchmarking solutions through a series of challenges.
1 code implementation • CVPR 2019 • Samvit Jain, Xin Wang, Joseph Gonzalez
We present Accel, a novel semantic video segmentation system that achieves high accuracy at low inference cost by combining the predictions of two network branches: (1) a reference branch that extracts high-detail features on a reference keyframe, and warps these features forward using frame-to-frame optical flow estimates, and (2) an update branch that computes features of adjustable quality on the current frame, performing a temporal update at each video frame.
1 code implementation • 3 Apr 2018 • Lauri Juvela, Bajibabu Bollepalli, Xin Wang, Hirokazu Kameoka, Manu Airaksinen, Junichi Yamagishi, Paavo Alku
This paper proposes a method for generating speech from filterbank mel frequency cepstral coefficients (MFCC), which are widely used in speech applications, such as ASR, but are generally considered unusable for speech synthesis.
1 code implementation • CVPR 2019 • Xin Wang, Fisher Yu, Ruth Wang, Trevor Darrell, Joseph E. Gonzalez
We show that TAFE-Net is highly effective in generalizing to new tasks or concepts and evaluate the TAFE-Net on a range of benchmarks in zero-shot and few-shot learning.
Ranked #1 on Few-Shot Image Classification on aPY - 0-Shot
1 code implementation • 11 Jun 2019 • Xin Wang, Fisher Yu, Trevor Darrell, Joseph E. Gonzalez
In this work, we propose a task-aware feature generation (TFG) framework for compositional learning, which generates features of novel visual concepts by transferring knowledge from previously seen concepts.
2 code implementations • ICCV 2019 • Xin Wang, Jiawei Wu, Junkun Chen, Lei LI, Yuan-Fang Wang, William Yang Wang
We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context.
2 code implementations • 8 Jan 2020 • Pengda Qin, Xin Wang, Wenhu Chen, Chunyun Zhang, Weiran Xu, William Yang Wang
Large-scale knowledge graphs (KGs) are shown to become more important in current information systems.
1 code implementation • 4 Apr 2021 • Chang Zeng, Xin Wang, Erica Cooper, Xiaoxiao Miao, Junichi Yamagishi
Probabilistic linear discriminant analysis (PLDA) or cosine similarity have been widely used in traditional speaker verification systems as back-end techniques to measure pairwise similarities.
Ranked #1 on Speaker Verification on CN-CELEB
1 code implementation • 22 Feb 2021 • Yudong Chen, Chaoyu Guan, Zhikun Wei, Xin Wang, Wenwu Zhu
Meta-learning aims at learning quickly on novel tasks with limited data by transferring generic experience learned from previous tasks.
1 code implementation • 14 Dec 2021 • Ziwei Luo, Jing Hu, Xin Wang, Shu Hu, Bin Kong, Youbing Yin, Qi Song, Xi Wu, Siwei Lyu
We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations.
1 code implementation • 14 Dec 2021 • Ziwei Luo, Jing Hu, Xin Wang, Siwei Lyu, Bin Kong, Youbing Yin, Qi Song, Xi Wu
Training a model-free deep reinforcement learning model to solve image-to-image translation is difficult since it involves high-dimensional continuous state and action spaces.
1 code implementation • SIGKDD 2022 • Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, Xin Wang
Based on the pre-trained model, we propose the graph prompting function to modify the standalone node into a token pair, and reformulate the downstream node classification looking the same as edge prediction.
1 code implementation • 24 Sep 2023 • Xin Wang, Ziwei Luo, Jing Hu, Chengming Feng, Shu Hu, Bin Zhu, Xi Wu, Xin Li, Siwei Lyu
The key feature in the RL-I2IT framework is to decompose a monolithic learning process into small steps with a lightweight model to progressively transform a source image successively to a target image.
1 code implementation • 29 Oct 2018 • Shinji Takaki, Toru Nakashika, Xin Wang, Junichi Yamagishi
This paper proposes a new loss using short-time Fourier transform (STFT) spectra for the aim of training a high-performance neural speech waveform model that predicts raw continuous speech waveform samples directly.
1 code implementation • ICCV 2021 • Sheng Zhou, Yucheng Wang, Defang Chen, Jiawei Chen, Xin Wang, Can Wang, Jiajun Bu
The holistic knowledge is represented as a unified graph-based embedding by aggregating individual knowledge from relational neighborhood samples with graph neural networks, the student network is learned by distilling the holistic knowledge in a contrastive manner.
2 code implementations • 14 Dec 2022 • Chengzhi Mao, Scott Geng, Junfeng Yang, Xin Wang, Carl Vondrick
We apply this training loss to two adaption methods, model finetuning and visual prompt tuning.
2 code implementations • ACL 2019 • Hong Wang, Xin Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang
Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss, which does not explicitly capture the global context at the document level.
1 code implementation • 2 Dec 2019 • Paola Garcia, Jesus Villalba, Herve Bredin, Jun Du, Diego Castan, Alejandrina Cristia, Latane Bullock, Ling Guo, Koji Okabe, Phani Sankar Nidadavolu, Saurabh Kataria, Sizhu Chen, Leo Galmant, Marvin Lavechin, Lei Sun, Marie-Philippe Gill, Bar Ben-Yair, Sajjad Abdoli, Xin Wang, Wassim Bouaziz, Hadrien Titeux, Emmanuel Dupoux, Kong Aik Lee, Najim Dehak
This paper presents the problems and solutions addressed at the JSALT workshop when using a single microphone for speaker detection in adverse scenarios.
Audio and Speech Processing Sound
1 code implementation • EMNLP 2018 • Wenhu Chen, Jianshu Chen, Yu Su, Xin Wang, Dong Yu, Xifeng Yan, William Yang Wang
Then, we pre-train a state tracker for the source language as a teacher, which is able to exploit easy-to-access parallel data.
1 code implementation • 10 Nov 2019 • Seyyed Saeed Sarfjoo, Xin Wang, Gustav Eje Henter, Jaime Lorenzo-Trueba, Shinji Takaki, Junichi Yamagishi
Nowadays vast amounts of speech data are recorded from low-quality recorder devices such as smartphones, tablets, laptops, and medium-quality microphones.
Sound Audio and Speech Processing
2 code implementations • CVPR 2017 • Xin Wang, Geoffrey Oxholm, Da Zhang, Yuan-Fang Wang
That is, our scheme can generate results that are visually pleasing and more similar to multiple desired artistic styles with color and texture cues at multiple scales.
1 code implementation • 25 Nov 2020 • Anzhu Yu, Wenyue Guo, Bing Liu, Xin Chen, Xin Wang, Xuefeng Cao, Bingchuan Jiang
This strategy estimates the depth map at coarsest level, while the depth maps at finer levels are considered as the upsampled depth map from previous level with pixel-wise depth residual.
1 code implementation • IJCNLP 2019 • Ming Jiang, Qiuyuan Huang, Lei Zhang, Xin Wang, Pengchuan Zhang, Zhe Gan, Jana Diesner, Jianfeng Gao
This paper presents a new metric called TIGEr for the automatic evaluation of image captioning systems.
1 code implementation • 23 Apr 2022 • Baifeng Shi, Yale Song, Neel Joshi, Trevor Darrell, Xin Wang
We present VARS, Visual Attention from Recurrent Sparse reconstruction, a new attention formulation built on two prominent features of the human visual attention mechanism: recurrency and sparsity.
1 code implementation • 11 Nov 2022 • Zeyu Hu, Xuyang Bai, Runze Zhang, Xin Wang, Guangyuan Sun, Hongbo Fu, Chiew-Lan Tai
We propose LiDAL, a novel active learning method for 3D LiDAR semantic segmentation by exploiting inter-frame uncertainty among LiDAR frames.
1 code implementation • 22 Jan 2024 • Li Lin, Neeraj Gupta, Yue Zhang, Hainan Ren, Chun-Hao Liu, Feng Ding, Xin Wang, Xin Li, Luisa Verdoliva, Shu Hu
The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and large language models, has marked a new era where AI-generated multimedia is increasingly integrated into various aspects of daily life.
1 code implementation • NAACL 2018 • Xin Wang, Yuan-Fang Wang, William Yang Wang
Furthermore, for the first time, we validate the superior performance of the deep audio features on the video captioning task.
1 code implementation • 23 Aug 2020 • Jianxin Ma, Chang Zhou, Hongxia Yang, Peng Cui, Xin Wang, Wenwu Zhu
There exist two challenges: i) reconstructing a future sequence containing many behaviors is exponentially harder than reconstructing a single next behavior, which can lead to difficulty in convergence, and ii) the sequence of all future behaviors can involve many intentions, not all of which may be predictable from the sequence of earlier behaviors.
1 code implementation • 8 Oct 2020 • Xin Wang, Jie Ren, Shuyun Lin, Xiangming Zhu, Yisen Wang, Quanshi Zhang
We discover and prove the negative correlation between the adversarial transferability and the interaction inside adversarial perturbations.
1 code implementation • CVPR 2023 • Chengzhi Mao, Revant Teotia, Amrutha Sundar, Sachit Menon, Junfeng Yang, Xin Wang, Carl Vondrick
We propose a ``doubly right'' object recognition benchmark, where the metric requires the model to simultaneously produce both the right labels as well as the right rationales.
1 code implementation • 5 May 2023 • Hong Chen, YiPeng Zhang, Simin Wu, Xin Wang, Xuguang Duan, Yuwei Zhou, Wenwu Zhu
To tackle the problems, we propose DisenBooth, an identity-preserving disentangled tuning framework for subject-driven text-to-image generation.
1 code implementation • 18 Jun 2022 • Yijian Qin, Ziwei Zhang, Xin Wang, Zeyang Zhang, Wenwu Zhu
To the best of our knowledge, our work is the first benchmark for graph neural architecture search.
1 code implementation • 14 Jan 2021 • Jinkun Cao, Xin Wang, Trevor Darrell, Fisher Yu
To decide the action at each step, we seek the action sequence that can lead to safe future states based on the prediction module outputs by repeatedly sampling likely action sequences.
1 code implementation • 22 Sep 2021 • Yuxiang Wu, Shang Wu, Xin Wang, Chengtian Lang, Quanshi Zhang, Quan Wen, Tianqi Xu
Second, 2-dimensional neuronal regions are fused into 3-dimensional neuron entities.
1 code implementation • NeurIPS 2021 • Hong Chen, Yudong Chen, Xin Wang, Ruobing Xie, Rui Wang, Feng Xia, Wenwu Zhu
However, learning such disentangled representations from multi-feedback data is challenging because i) multi-feedback is complex: there exist complex relations among different types of feedback (e. g., click, unclick, and dislike, etc) as well as various user intentions, and ii) multi-feedback is noisy: there exists noisy (useless) information both in features and labels, which may deteriorate the recommendation performance.
1 code implementation • 28 May 2023 • Lin Zhang, Xin Wang, Erica Cooper, Nicholas Evans, Junichi Yamagishi
To properly measure misclassified ranges and better evaluate spoof localization performance, we upgrade point-based EER to range-based EER.
1 code implementation • 12 Mar 2021 • Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang
This paper provides a unified view to explain different adversarial attacks and defense methods, i. e. the view of multi-order interactions between input variables of DNNs.
1 code implementation • 5 Nov 2021 • Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang
This paper provides a unified view to explain different adversarial attacks and defense methods, \emph{i. e.} the view of multi-order interactions between input variables of DNNs.
1 code implementation • NeurIPS 2021 • Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang
This paper provides a unified view to explain different adversarial attacks and defense methods, i. e. the view of multi-order interactions between input variables of DNNs.
1 code implementation • 7 Apr 2022 • Zeyang Zhang, Ziwei Zhang, Xin Wang, Wenwu Zhu
To solve these challenges, we first propose a principled hardness measurement to quantify the hardness of TSP instances.
2 code implementations • 6 Feb 2019 • Xiao-Ming Zhang, Zezhu Wei, Raza Asad, Xu-Chen Yang, Xin Wang
In this work, we perform a comparative study on the efficacy of three reinforcement learning algorithms: tabular Q-learning, deep Q-learning, and policy gradient, as well as two non-machine-learning methods: stochastic gradient descent and Krotov algorithms, in the problem of preparing a desired quantum state.
Quantum Physics
1 code implementation • 22 Aug 2020 • Geeticka Chauhan, Ruizhi Liao, William Wells, Jacob Andreas, Xin Wang, Seth Berkowitz, Steven Horng, Peter Szolovits, Polina Golland
To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time.
1 code implementation • 1 Feb 2023 • Luyi Han, Tao Tan, Tianyu Zhang, Yunzhi Huang, Xin Wang, Yuan Gao, Jonas Teuwen, Ritse Mann
Multi-sequence MRIs can be necessary for reliable diagnosis in clinical practice due to the complimentary information within sequences.
1 code implementation • 3 Jul 2023 • Luyi Han, Tianyu Zhang, Yunzhi Huang, Haoran Dou, Xin Wang, Yuan Gao, Chunyao Lu, Tan Tao, Ritse Mann
Multi-sequence MRI is valuable in clinical settings for reliable diagnosis and treatment prognosis, but some sequences may be unusable or missing for various reasons.
1 code implementation • 21 Jul 2018 • Da Zhang, Xiyang Dai, Xin Wang, Yuan-Fang Wang
In this paper, we present a novel Single Shot multi-Span Detector for temporal activity detection in long, untrimmed videos using a simple end-to-end fully three-dimensional convolutional (Conv3D) network.
1 code implementation • 18 Jun 2021 • Lin Wang, Lie Ju, Xin Wang, Wanji He, Donghao Zhang, Yelin Huang, Zhiwen Yang, Xuan Yao, Xin Zhao, Xiufen Ye, ZongYuan Ge
None of them investigate the influence of the ambiguous nature of the lesion itself. Inspired by image matting, this paper introduces alpha matte as a soft mask to represent uncertain areas in medical scenes and accordingly puts forward a new uncertainty quantification method to fill the gap of uncertainty research for lesion structure.
1 code implementation • 27 Jun 2017 • Xin Wang, Lejun Zou, Xiaohua Shen, Yupeng Ren, Yi Qin
In tests using outcrop point cloud data, the proposed method identified and extracted the full extent of individual fractures with high accuracy.
1 code implementation • ICCV 2021 • Jianren Wang, Xin Wang, Yue Shang-Guan, Abhinav Gupta
To bridge the gap, we present a new online continual object detection benchmark with an egocentric video dataset, Objects Around Krishna (OAK).
1 code implementation • 29 Nov 2022 • Paul-Gauthier Noé, Xiaoxiao Miao, Xin Wang, Junichi Yamagishi, Jean-François Bonastre, Driss Matrouf
The use of modern vocoders in an analysis/synthesis pipeline allows us to investigate high-quality voice conversion that can be used for privacy purposes.
1 code implementation • 12 Mar 2024 • Xin Wang, Yu Zheng, Zhongwei Wan, Mi Zhang
However, state-of-the-art SVD-based LLM compression methods have two key limitations: truncating smaller singular values may lead to higher compression loss, and the lack of update on the remaining model parameters after SVD truncation.
1 code implementation • 24 Nov 2022 • Xin Yang, Michael Bi Mi, Yuan Yuan, Xin Wang, Robby T. Tan
In our DA framework, we retain the depth and background information during the domain feature alignment.
1 code implementation • 31 Aug 2023 • Qiang Huang, Jiawei Jiang, Xi Susie Rao, Ce Zhang, Zhichao Han, Zitao Zhang, Xin Wang, Yongjun He, Quanqing Xu, Yang Zhao, Chuang Hu, Shuo Shang, Bo Du
To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed.
1 code implementation • 16 Jul 2020 • Lingwei Wei, Dou Hu, Wei Zhou, Xuehai Tang, Xiaodan Zhang, Xin Wang, Jizhong Han, Songlin Hu
Furthermore, we design a Sentiment-based Rethinking mechanism (SR) by refining the HIN with sentiment label information to learn a more sentiment-aware document representation.
1 code implementation • NeurIPS 2020 • Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu
In forming learning objectives, one oftentimes needs to aggregate a set of individual values to a single output.
1 code implementation • 7 Jun 2021 • Shu Hu, Yiming Ying, Xin Wang, Siwei Lyu
A combination loss of AoRR and TKML is proposed as a new learning objective for improving the robustness of multi-label learning in the face of outliers in sample and labels alike.
1 code implementation • 23 Sep 2021 • Kai Guo, Kaixiong Zhou, Xia Hu, Yu Li, Yi Chang, Xin Wang
Graph neural networks (GNNs) have received tremendous attention due to their superiority in learning node representations.
1 code implementation • 27 Feb 2024 • Li Lin, Xinan He, Yan Ju, Xin Wang, Feng Ding, Shu Hu
The existing method for addressing this problem is providing a fair loss function.
1 code implementation • NeurIPS 2021 • Heng Chang, Yu Rong, Tingyang Xu, Yatao Bian, Shiji Zhou, Xin Wang, Junzhou Huang, Wenwu Zhu
Graph Convolutional Networks (GCNs) are promising deep learning approaches in learning representations for graph-structured data.
1 code implementation • 11 Jul 2022 • Tyler LaBonte, Yale Song, Xin Wang, Vibhav Vineet, Neel Joshi
A critical object detection task is finetuning an existing model to detect novel objects, but the standard workflow requires bounding box annotations which are time-consuming and expensive to collect.
1 code implementation • 4 Jul 2023 • Zhenwei Zhang, Xin Wang, Jingyuan Xie, Heling Zhang, Yuantao Gu
Unlocking the potential of deep learning in Peak-Hour Series Forecasting (PHSF) remains a critical yet underexplored task in various domains.
1 code implementation • 16 Mar 2024 • Hanlei Zhang, Xin Wang, Hua Xu, Qianrui Zhou, Kai Gao, Jianhua Su, jinyue Zhao, Wenrui Li, Yanting Chen
We believe that MIntRec2. 0 will serve as a valuable resource, providing a pioneering foundation for research in human-machine conversational interactions, and significantly facilitating related applications.
1 code implementation • 16 Feb 2022 • Haoyang Li, Xin Wang, Ziwei Zhang, Wenwu Zhu
This paper is the first systematic and comprehensive review of OOD generalization on graphs, to the best of our knowledge.
1 code implementation • 20 Oct 2022 • Yanfei Xiang, Xin Wang, Shu Hu, Bin Zhu, Xiaomeng Huang, Xi Wu, Siwei Lyu
Reinforcement learning is applied to solve actual complex tasks from high-dimensional, sensory inputs.
1 code implementation • 19 Nov 2023 • Jiaming Zhang, Xingjun Ma, Xin Wang, Lingyu Qiu, Jiaqi Wang, Yu-Gang Jiang, Jitao Sang
With the rapid advancement of multimodal learning, pre-trained Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable capacities in bridging the gap between visual and language modalities.
1 code implementation • 23 Dec 2023 • Hengrui Gu, Kaixiong Zhou, Xiaotian Han, Ninghao Liu, Ruobing Wang, Xin Wang
Multi-hop question answering (MQA) is one of the challenging tasks to evaluate machine's comprehension and reasoning abilities, where large language models (LLMs) have widely achieved the human-comparable performance.
1 code implementation • 5 Feb 2024 • Zexin Wang, Changhua Pei, Minghua Ma, Xin Wang, Zhihan Li, Dan Pei, Saravan Rajmohan, Dongmei Zhang, QIngwei Lin, Haiming Zhang, Jianhui Li, Gaogang Xie
To ensure an accurate AD, FCVAE exploits an innovative approach to concurrently integrate both the global and local frequency features into the condition of Conditional Variational Autoencoder (CVAE) to significantly increase the accuracy of reconstructing the normal data.
1 code implementation • 31 Jul 2021 • Shu Hu, Lipeng Ke, Xin Wang, Siwei Lyu
Top-$k$ multi-label learning, which returns the top-$k$ predicted labels from an input, has many practical applications such as image annotation, document analysis, and web search engine.
1 code implementation • ICCV 2021 • Shu Hu, Lipeng Ke, Xin Wang, Siwei Lyu
Top-k multi-label learning, which returns the top-k predicted labels from an input, has many practical applications such as image annotation, document analysis, and web search engine.
1 code implementation • 26 May 2023 • Zheng Li, Caili Guo, Xin Wang, Zerun Feng, Yanjun Wang
Given a query caption, the goal is to rank candidate images by relevance, from large to small.
1 code implementation • 9 May 2019 • Chenghao Liu, Tao Lu, Xin Wang, Zhiyong Cheng, Jianling Sun, Steven C. H. Hoi
However, CF with binary codes naturally suffers from low accuracy due to limited representation capability in each bit, which impedes it from modeling complex structure of the data.
1 code implementation • 17 Dec 2021 • Xin Wang, Pei Guo, Xingyan Li, Aryya Gangopadhyay, Carl E. Busart, Jade Freeman, Jianwu Wang
To tackle these problems, we leverage serverless computing and containerization techniques for automated scalable execution and reproducibility, and utilize the adapter design pattern to enable application portability and reproducibility across different clouds.
1 code implementation • 11 May 2022 • Guangxi Li, Xuanqiang Zhao, Xin Wang
An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial intelligence, including natural language processing (NLP).
1 code implementation • 8 Feb 2024 • Meihan Liu, Zeyu Fang, Zhen Zhang, Ming Gu, Sheng Zhou, Xin Wang, Jiajun Bu
Motivated by our empirical analysis, we reevaluate the role of GNNs in graph domain adaptation and uncover the pivotal role of the propagation process in GNNs for adapting to different graph domains.
1 code implementation • Findings (ACL) 2021 • Canasai Kruengkrai, Junichi Yamagishi, Xin Wang
Evidence-based fact checking aims to verify the truthfulness of a claim against evidence extracted from textual sources.
1 code implementation • 25 Apr 2019 • Han Xu, Junning Li, Liqiang Liu, Yu Wang, Haidong Yuan, Xin Wang
Measurement and estimation of parameters are essential for science and engineering, where one of the main quests is to find systematic schemes that can achieve high precision.
Quantum Physics Mesoscale and Nanoscale Physics
1 code implementation • 7 Jan 2021 • Aite Zhao, Jianbo Li, Junyu Dong, Lin Qi, Qianni Zhang, Ning li, Xin Wang, Huiyu Zhou
In recent years, single modality based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognised that each of the established approaches has different strengths and weaknesses.
1 code implementation • 11 Jun 2021 • Tomi Kinnunen, Andreas Nautsch, Md Sahidullah, Nicholas Evans, Xin Wang, Massimiliano Todisco, Héctor Delgado, Junichi Yamagishi, Kong Aik Lee
Whether it be for results summarization, or the analysis of classifier fusion, some means to compare different classifiers can often provide illuminating insight into their behaviour, (dis)similarity or complementarity.
1 code implementation • 3 Feb 2023 • Tianyu Zhang, Tao Tan, Luyi Han, Xin Wang, Yuan Gao, Jonas Teuwen, Regina Beets-Tan, Ritse Mann
Then the multi-parameter fusion with attention module enables the interaction of the encoded information from different parameters through a set of algorithmic strategies, and applies different weights to the information through the attention mechanism after information fusion to obtain refined representation information.
1 code implementation • 3 Jul 2023 • Tianyu Zhang, Luyi Han, Anna D'Angelo, Xin Wang, Yuan Gao, Chunyao Lu, Jonas Teuwen, Regina Beets-Tan, Tao Tan, Ritse Mann
DWIs with different b-values are fused to efficiently utilize the difference features of DWIs.
1 code implementation • ICCV 2023 • Yucheng Xing, Xin Wang
Considering the structural-property of the skeleton data in representing human poses and the possible irregularity caused by occlusion, we propose the use of dynamic graph convolution as the basic operator.
1 code implementation • 17 Jan 2024 • Luyi Han, Tao Tan, Tianyu Zhang, Yuan Gao, Xin Wang, Valentina Longo, Sofía Ventura-Díaz, Anna D'Angelo, Jonas Teuwen, Ritse Mann
We use a clinical dataset with 1630 MRI scans from 314 patients treated with NAC.
1 code implementation • NeurIPS 2023 • Zeyang Zhang, Xin Wang, Ziwei Zhang, Zhou Qin, Weigao Wen, Hui Xue, Haoyang Li, Wenwu Zhu
In this paper, we discover that there exist cases with distribution shifts unobservable in the time domain while observable in the spectral domain, and propose to study distribution shifts on dynamic graphs in the spectral domain for the first time.
1 code implementation • IEEE 2015 • Xin Wang, Devinder Kumar, Nicolas Thome, Matthieu Cord, Frederic Precioso
We present deep experiments of recipe recognition on our dataset using visual, textual information and fusion.
1 code implementation • 6 Apr 2021 • Xin Wang, Yang Zhao, Tangwen Yang, Qiuqi Ruan
In this paper, we propose a multi-scale context aggregation network (MSCANet) based on single-column encoder-decoder architecture for crowd counting, which consists of an encoder based on a dense context-aware module (DCAM) and a hierarchical attention-guided decoder.
1 code implementation • 8 Jul 2022 • Wei Feng, Lin Wang, Lie Ju, Xin Zhao, Xin Wang, Xiaoyu Shi, ZongYuan Ge
Existing unsupervised domain adaptation methods based on adversarial learning have achieved good performance in several medical imaging tasks.
1 code implementation • 22 Jul 2022 • Fenia Christopoulou, Gerasimos Lampouras, Milan Gritta, Guchun Zhang, Yinpeng Guo, Zhongqi Li, Qi Zhang, Meng Xiao, Bo Shen, Lin Li, Hao Yu, Li Yan, Pingyi Zhou, Xin Wang, Yuchi Ma, Ignacio Iacobacci, Yasheng Wang, Guangtai Liang, Jiansheng Wei, Xin Jiang, Qianxiang Wang, Qun Liu
We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i. e. the synthesis of programming language solutions given a natural language problem description.
1 code implementation • 16 Aug 2022 • Gur-Eyal Sela, Ionel Gog, Justin Wong, Kumar Krishna Agrawal, Xiangxi Mo, Sukrit Kalra, Peter Schafhalter, Eric Leong, Xin Wang, Bharathan Balaji, Joseph Gonzalez, Ion Stoica
These works evaluate accuracy offline, one image at a time.
1 code implementation • 10 Sep 2023 • Shu Hu, Zhenhuan Yang, Xin Wang, Yiming Ying, Siwei Lyu
Theoretically, we show that the learning objective of ORAT satisfies the $\mathcal{H}$-consistency in binary classification, which establishes it as a proper surrogate to adversarial 0/1 loss.
1 code implementation • 21 Sep 2023 • Zongqian Zhan, Rui Xia, Yifei Yu, Yibo Xu, Xin Wang
Over the last decades, ample achievements have been made on Structure from motion (SfM).
1 code implementation • 13 Aug 2020 • Steven Horng, Ruizhi Liao, Xin Wang, Sandeep Dalal, Polina Golland, Seth J. Berkowitz
Results: The area under the receiver operating characteristic curve (AUC) for differentiating alveolar edema from no edema was 0. 99 for the semi-supervised model and 0. 87 for the pre-trained models.
1 code implementation • 15 Oct 2021 • Xin Wang
The quantum circuits are comprised with single-qubit rotation gates implementing on each qubit.
1 code implementation • Interspeech 2023 • Chang Zeng, Xin Wang, Xiaoxiao Miao, Erica Cooper, Junichi Yamagishi
The ability of countermeasure models to generalize from seen speech synthesis methods to unseen ones has been investigated in the ASVspoof challenge.
1 code implementation • 3 Aug 2023 • Liang Peng, Xin Wang, Xiaofeng Zhu
Unsupervised multiplex graph learning (UMGL) has been shown to achieve significant effectiveness for different downstream tasks by exploring both complementary information and consistent information among multiple graphs.
1 code implementation • ACM International Conference on Information & Knowledge Management (CIKM) 2022 • Yili Wang, Kaixiong Zhou, Rui Miao, Ninghao Liu, Xin Wang
To bridge the gap between large-scale graph training and contrastive learning, we propose adaptive subgraph contrastive learning (AdaGCL).
1 code implementation • 13 Mar 2024 • Li Lin, Yamini Sri Krubha, Zhenhuan Yang, Cheng Ren, Thuc Duy Le, Irene Amerini, Xin Wang, Shu Hu
In the realm of medical imaging, particularly for COVID-19 detection, deep learning models face substantial challenges such as the necessity for extensive computational resources, the paucity of well-annotated datasets, and a significant amount of unlabeled data.
1 code implementation • 14 Mar 2024 • Li Lin, Sarah Papabathini, Xin Wang, Shu Hu
Human affective behavior analysis aims to delve into human expressions and behaviors to deepen our understanding of human emotions.
no code implementations • 5 Jun 2018 • Xin Wang, Fisher Yu, Lisa Dunlap, Yi-An Ma, Ruth Wang, Azalia Mirhoseini, Trevor Darrell, Joseph E. Gonzalez
Larger networks generally have greater representational power at the cost of increased computational complexity.
no code implementations • 18 Apr 2018 • T. Anderson Keller, Sharath Nittur Sridhar, Xin Wang
Associative memory using fast weights is a short-term memory mechanism that substantially improves the memory capacity and time scale of recurrent neural networks (RNNs).
no code implementations • 7 Apr 2018 • Xin Wang, Jaime Lorenzo-Trueba, Shinji Takaki, Lauri Juvela, Junichi Yamagishi
Recent advances in speech synthesis suggest that limitations such as the lossy nature of the amplitude spectrum with minimum phase approximation and the over-smoothing effect in acoustic modeling can be overcome by using advanced machine learning approaches.
no code implementations • CVPR 2018 • Xin Wang, Wenhu Chen, Jiawei Wu, Yuan-Fang Wang, William Yang Wang
Video captioning is the task of automatically generating a textual description of the actions in a video.
Hierarchical Reinforcement Learning reinforcement-learning +2
no code implementations • 2 Mar 2018 • Jaime Lorenzo-Trueba, Fuming Fang, Xin Wang, Isao Echizen, Junichi Yamagishi, Tomi Kinnunen
Thanks to the growing availability of spoofing databases and rapid advances in using them, systems for detecting voice spoofing attacks are becoming more and more capable, and error rates close to zero are being reached for the ASVspoof2015 database.
no code implementations • NeurIPS 2017 • Urs Köster, Tristan J. Webb, Xin Wang, Marcel Nassar, Arjun K. Bansal, William H. Constable, Oğuz H. Elibol, Scott Gray, Stewart Hall, Luke Hornof, Amir Khosrowshahi, Carey Kloss, Ruby J. Pai, Naveen Rao
Here we present the Flexpoint data format, aiming at a complete replacement of 32-bit floating point format training and inference, designed to support modern deep network topologies without modifications.
no code implementations • 3 Jun 2017 • Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez
Advances in deep learning have led to substantial increases in prediction accuracy but have been accompanied by increases in the cost of rendering predictions.
no code implementations • 31 Jan 2017 • Da Zhang, Hamid Maei, Xin Wang, Yuan-Fang Wang
In this paper we introduce a fully end-to-end approach for visual tracking in videos that learns to predict the bounding box locations of a target object at every frame.
no code implementations • 9 Dec 2016 • Daniel Crankshaw, Xin Wang, Giulio Zhou, Michael J. Franklin, Joseph E. Gonzalez, Ion Stoica
In this paper, we introduce Clipper, a general-purpose low-latency prediction serving system.
no code implementations • 7 Oct 2016 • Tianyi Chen, Aryan Mokhtari, Xin Wang, Alejandro Ribeiro, Georgios B. Giannakis
Existing approaches to resource allocation for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements.
no code implementations • 8 Dec 2016 • Ioannis Papavasileiou, Wenlong Zhang, Xin Wang, Jinbo Bi, Li Zhang, Song Han
An advanced machine learning method, multi-task feature learning (MTFL), is used to jointly train classification models of a subject's gait in three classes, post-stroke, PD and healthy gait.
no code implementations • 21 Dec 2016 • Badong Chen, Lei Xing, Xin Wang, Jing Qin, Nanning Zheng
Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing.
no code implementations • NeurIPS 2014 • Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun
We prove that this framework is mathematically equivalent to the widely used multitask feature learning methods that are based on a joint regularization of all model parameters, but with a more general form of regularizers.
no code implementations • 19 Oct 2016 • Xin Wang, Siu Ming Yiu
Based on API call sequences, semantic-aware and machine learning (ML) based malware classifiers can be built for malware detection or classification.
no code implementations • 30 Jul 2018 • Gustav Eje Henter, Jaime Lorenzo-Trueba, Xin Wang, Junichi Yamagishi
Generating versatile and appropriate synthetic speech requires control over the output expression separate from the spoken text.
no code implementations • ECCV 2018 • Wei Dong, Qiuyuan Wang, Xin Wang, Hongbin Zha
We propose a novel 3D spatial representation for data fusion and scene reconstruction.
no code implementations • 2 Aug 2018 • Hieu-Thi Luong, Xin Wang, Junichi Yamagishi, Nobuyuki Nishizawa
We investigated the impact of noisy linguistic features on the performance of a Japanese speech synthesis system based on neural network that uses WaveNet vocoder.
1 code implementation • 19 Jun 2017 • Kun Fang, Xin Wang, Marco Tomamichel, Runyao Duan
For isotropic states, it can be further simplified to a linear program.
Quantum Physics
no code implementations • 29 Oct 2018 • Xin Wang, Shinji Takaki, Junichi Yamagishi
Neural waveform models such as the WaveNet are used in many recent text-to-speech systems, but the original WaveNet is quite slow in waveform generation because of its autoregressive (AR) structure.
no code implementations • 29 Oct 2018 • Fuming Fang, Xin Wang, Junichi Yamagishi, Isao Echizen
Transforming the facial and acoustic features together makes it possible for the converted voice and facial expressions to be highly correlated and for the generated target speaker to appear and sound natural.
no code implementations • 7 Nov 2018 • Xin Wang, Jiawei Wu, Da Zhang, Yu Su, William Yang Wang
Although promising results have been achieved in video captioning, existing models are limited to the fixed inventory of activities in the training corpus, and do not generalize to open vocabulary scenarios.
no code implementations • CVPR 2019 • Xin Wang, Qiuyuan Huang, Asli Celikyilmaz, Jianfeng Gao, Dinghan Shen, Yuan-Fang Wang, William Yang Wang, Lei Zhang
Vision-language navigation (VLN) is the task of navigating an embodied agent to carry out natural language instructions inside real 3D environments.
Ranked #2 on Vision-Language Navigation on Room2Room
no code implementations • 25 Nov 2018 • Fei Xue, Qiuyuan Wang, Xin Wang, Wei Dong, Junqiu Wang, Hongbin Zha
We present a novel end-to-end visual odometry architecture with guided feature selection based on deep convolutional recurrent neural networks.
no code implementations • CVPR 2019 • Da Zhang, Xiyang Dai, Xin Wang, Yuan-Fang Wang, Larry S. Davis
In this paper, we present Moment Alignment Network (MAN), a novel framework that unifies the candidate moment encoding and temporal structural reasoning in a single-shot feed-forward network.
no code implementations • 13 Dec 2018 • Marcel Nassar, Xin Wang, Evren Tumer
Thus, we refer to our model as Conditional Graph Neural Process (CGNP).
no code implementations • 18 Dec 2018 • Quanshi Zhang, Xin Wang, Ruiming Cao, Ying Nian Wu, Feng Shi, Song-Chun Zhu
This paper introduces a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside conv-layers of a pre-trained CNN.
no code implementations • WS 2017 • Jianan Wang, Xin Wang, Fang Li, Zhen Xu, Zhuoran Wang, Baoxun Wang
For practical chatbots, one of the essential factor for improving user experience is the capability of customizing the talking style of the agents, that is, to make chatbots provide responses meeting users{'} preference on language styles, topics, etc.
no code implementations • IJCNLP 2017 • Xin Wang, Jianan Wang, Yuanchao Liu, Xiaolong Wang, Zhuoran Wang, Baoxun Wang
Besides, strategies of obtaining distance supervision data for pre-training are also discussed in this work.
no code implementations • NeurIPS 2013 • Siwei Lyu, Xin Wang
Nonnegative matrix factorization (NMF) is a popular data analysis method, the objective of which is to decompose a matrix with all nonnegative components into the product of two other nonnegative matrices.
no code implementations • 8 Jan 2019 • Quanshi Zhang, Xin Wang, Ying Nian Wu, Huilin Zhou, Song-Chun Zhu
This paper proposes a generic method to learn interpretable convolutional filters in a deep convolutional neural network (CNN) for object classification, where each interpretable filter encodes features of a specific object part.
no code implementations • 21 Dec 2018 • Eric Wu, Bin Kong, Xin Wang, Junjie Bai, Yi Lu, Feng Gao, Shaoting Zhang, Kunlin Cao, Qi Song, Siwei Lyu, Youbing Yin
The hierarchical attention components of the residual attention subnet force our network to focus on the key components of the X-ray images and generate the final predictions as well as the associated visual supports, which is similar to the assessment procedure of clinicians.
no code implementations • ACL 2019 • Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang, Liqun Chen, Xin Wang, Jianfeng Gao, Lawrence Carin
Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables.
no code implementations • 15 Feb 2019 • Hesham Mostafa, Xin Wang
We evaluate the performance of dynamic reallocation methods in training deep convolutional networks and show that our method outperforms previous static and dynamic reparameterization methods, yielding the best accuracy for a fixed parameter budget, on par with accuracies obtained by iteratively pruning a pre-trained dense model.
no code implementations • 29 Jan 2019 • Bin Kong, Xin Wang, Junjie Bai, Yi Lu, Feng Gao, Kunlin Cao, Qi Song, Shaoting Zhang, Siwei Lyu, Youbing Yin
In order to address these limitations, we present tree-structured ConvLSTM models for tree-structured image analysis tasks which can be trained end-to-end.
no code implementations • CVPR 2019 • Peixi Xiong, Huayi Zhan, Xin Wang, Baivab Sinha, Ying Wu
Based on GEA and Q, we provide techniques to find matches of Q in GEA, as the answer of Qnl in Img.
no code implementations • 25 Mar 2019 • Zhihui Guo, Junjie Bai, Yi Lu, Xin Wang, Kunlin Cao, Qi Song, Milan Sonka, Youbing Yin
The proposed method generates well-positioned centerlines, exhibiting lower number of missing branches and is more robust in the presence of minor imperfections of the object segmentation mask.
no code implementations • 29 Mar 2019 • Mingyang Zhang, Xin Wang, Fuming Fang, Haizhou Li, Junichi Yamagishi
We propose using an extended model architecture of Tacotron, that is a multi-source sequence-to-sequence model with a dual attention mechanism as the shared model for both the TTS and VC tasks.
no code implementations • 1 Apr 2019 • Hieu-Thi Luong, Xin Wang, Junichi Yamagishi, Nobuyuki Nishizawa
When the available data of a target speaker is insufficient to train a high quality speaker-dependent neural text-to-speech (TTS) system, we can combine data from multiple speakers and train a multi-speaker TTS model instead.
no code implementations • CVPR 2019 • Fei Xue, Xin Wang, Shunkai Li, Qiuyuan Wang, Junqiu Wang, Hongbin Zha
Most previous learning-based visual odometry (VO) methods take VO as a pure tracking problem.
no code implementations • NAACL 2019 • Jiawei Wu, Xin Wang, William Yang Wang
The overreliance on large parallel corpora significantly limits the applicability of machine translation systems to the majority of language pairs.
no code implementations • ICCV 2019 • Zuxuan Wu, Xin Wang, Joseph E. Gonzalez, Tom Goldstein, Larry S. Davis
However, neural classifiers are often extremely brittle when confronted with domain shift---changes in the input distribution that occur over time.
no code implementations • 13 Apr 2019 • Badong Chen, Xin Wang, Yingsong Li, Jose C. Principe
The kernel function in correntropy is usually restricted to the Gaussian function with center located at zero.
no code implementations • 29 Apr 2019 • Mingming Sun, Xu Li, Xin Wang, Miao Fan, Yue Feng, Ping Li
In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain.
no code implementations • 27 Apr 2019 • Xin Wang, Shinji Takaki, Junichi Yamagishi
Other models such as Parallel WaveNet and ClariNet bring together the benefits of AR and IAF-based models and train an IAF model by transferring the knowledge from a pre-trained AR teacher to an IAF student without any sequential transformation.