no code implementations • 9 Feb 2025 • Xinyu Liu, Ailing Zeng, Wei Xue, Harry Yang, Wenhan Luo, Qifeng Liu, Yike Guo
Crafting magic and illusions is one of the most thrilling aspects of filmmaking, with visual effects (VFX) serving as the powerhouse behind unforgettable cinematic experiences.
no code implementations • 31 Jan 2025 • Xinyu Liu, Zixuan Xie, Shangtong Zhang
As a side product, we also use this general result to establish the $L^2$ convergence rate of tabular $Q$-learning with an $\epsilon$-softmax behavior policy, for which we rely on a novel pseudo-contraction property of the weighted Bellman optimality operator.
no code implementations • 29 Jan 2025 • Kunrong Li, Xinyu Liu, Zhen Chen
Inspired by the ability of pretrained Large Language Models (LLMs) in following instructions and generating coherent text, we propose a Semantic Consistency Regularization with Large Language Models (SCR) framework for semi-supervised sentiment analysis.
1 code implementation • the ACM Web Conference 2025 • Junhong Lian, Xiang Ao, Xinyu Liu, Yang Liu, Qing He
Prevailing methods focus on user-oriented content preferences, but most of them overlook the fact that diverse stylistic preferences are integral to users' panoramic interests, leading to suboptimal personalization.
no code implementations • 30 Dec 2024 • Shangyu Xing, Changhao Xiang, Yuteng Han, Yifan Yue, Zhen Wu, Xinyu Liu, Zhangtai Wu, Fei Zhao, Xinyu Dai
To address this limitation, we introduce GePBench, a novel benchmark designed to assess the geometric perception capabilities of MLLMs.
no code implementations • 20 Nov 2024 • Xiaochi Qian, Zixuan Xie, Xinyu Liu, Shangtong Zhang
As applications, we provide the first almost sure convergence rate for $Q$-learning with Markovian samples without count-based learning rates.
no code implementations • 13 Oct 2024 • Jingyu Liu, Xinyu Liu, Mingzhe Qu, Tianyi Lyu
To overcome these challenges, we propose the EITNet model, a deep learning framework that combines EfficientDet for object detection, I3D for spatiotemporal feature extraction, and TimeSformer for temporal analysis, all integrated with IoT technology for seamless real-time data collection and processing.
1 code implementation • 7 Oct 2024 • Xinyu Liu, Runsong Zhao, Pengcheng Huang, Chunyang Xiao, Bei Li, Jingang Wang, Tong Xiao, Jingbo Zhu
We provide an extensive survey for limitations in this work and propose a new method called forgetting curve to measure the memorization capability of long-context models.
no code implementations • 22 Sep 2024 • Runsong Zhao, Pengcheng Huang, Xinyu Liu, Chunyang Xiao, Tong Xiao, Jingbo Zhu
Compressing Transformer inputs into compressd tokens allows running LLMs with improved speed and cost efficiency.
1 code implementation • 16 Sep 2024 • Anthony Cioppa, Silvio Giancola, Vladimir Somers, Victor Joos, Floriane Magera, Jan Held, Seyed Abolfazl Ghasemzadeh, Xin Zhou, Karolina Seweryn, Mateusz Kowalczyk, Zuzanna Mróz, Szymon Łukasik, Michał Hałoń, Hassan Mkhallati, Adrien Deliège, Carlos Hinojosa, Karen Sanchez, Amir M. Mansourian, Pierre Miralles, Olivier Barnich, Christophe De Vleeschouwer, Alexandre Alahi, Bernard Ghanem, Marc Van Droogenbroeck, Adam Gorski, Albert Clapés, Andrei Boiarov, Anton Afanasiev, Artur Xarles, Atom Scott, Byoungkwon Lim, Calvin Yeung, Cristian Gonzalez, Dominic Rüfenacht, Enzo Pacilio, Fabian Deuser, Faisal Sami Altawijri, Francisco Cachón, Hankyul Kim, Haobo Wang, Hyeonmin Choe, Hyunwoo J Kim, Il-Min Kim, Jae-Mo Kang, Jamshid Tursunboev, Jian Yang, Jihwan Hong, JiMin Lee, Jing Zhang, Junseok Lee, Kexin Zhang, Konrad Habel, Licheng Jiao, Linyi Li, Marc Gutiérrez-Pérez, Marcelo Ortega, Menglong Li, Milosz Lopatto, Nikita Kasatkin, Nikolay Nemtsev, Norbert Oswald, Oleg Udin, Pavel Kononov, Pei Geng, Saad Ghazai Alotaibi, Sehyung Kim, Sergei Ulasen, Sergio Escalera, Shanshan Zhang, Shuyuan Yang, Sunghwan Moon, Thomas B. Moeslund, Vasyl Shandyba, Vladimir Golovkin, Wei Dai, WonTaek Chung, Xinyu Liu, Yongqiang Zhu, Youngseo Kim, Yuan Li, Yuting Yang, Yuxuan Xiao, Zehua Cheng, Zhihao LI
The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team.
no code implementations • 16 Sep 2024 • Jinlong Li, Xinyu Liu, Baolu Li, Runsheng Xu, Jiachen Li, Hongkai Yu, Zhengzhong Tu
Cooperative perception systems play a vital role in enhancing the safety and efficiency of vehicular autonomy.
no code implementations • 9 Sep 2024 • Henghui Ding, Lingyi Hong, Chang Liu, Ning Xu, Linjie Yang, Yuchen Fan, Deshui Miao, Yameng Gu, Xin Li, Zhenyu He, YaoWei Wang, Ming-Hsuan Yang, Jinming Chai, Qin Ma, Junpei Zhang, Licheng Jiao, Fang Liu, Xinyu Liu, Jing Zhang, Kexin Zhang, Xu Liu, Lingling Li, Hao Fang, Feiyu Pan, Xiankai Lu, Wei zhang, Runmin Cong, Tuyen Tran, Bin Cao, Yisi Zhang, Hanyi Wang, Xingjian He, Jing Liu
Despite the promising performance of current video segmentation models on existing benchmarks, these models still struggle with complex scenes.
no code implementations • 5 Sep 2024 • Chenglizhao Chen, Xinyu Liu, Mengke Song, Luming Li, Xu Yu, Shanchen Pang
In short, current methods struggle to integrate low-level visual and high-level action features, leading to poor anomaly detection in varied and complex scenes.
1 code implementation • 4 Sep 2024 • Xinyu Liu, Yingqing He, Lanqing Guo, Xiang Li, Bu Jin, Peng Li, Yan Li, Chi-Min Chan, Qifeng Chen, Wei Xue, Wenhan Luo, Qifeng Liu, Yike Guo
The hierarchical prompts offer both global and local guidance.
no code implementations • 30 Aug 2024 • Junhao Ruan, Abudukeyumu Abudula, Xinyu Liu, Bei Li, Yinqiao Li, Chenglong Wang, Yuchun Fan, Yuan Ge, Tong Xiao, Jingbo Zhu
In our work, we extend the critique of NTP, highlighting its limitation also due to training with a narrow objective: the prediction of a sub-optimal one-hot distribution.
1 code implementation • 27 Aug 2024 • Chi-Min Chan, Jianxuan Yu, Weize Chen, Chunyang Jiang, Xinyu Liu, Weijie Shi, Zhiyuan Liu, Wei Xue, Yike Guo
However, configuring an MAS for a task remains challenging, with performance only observable post-execution.
no code implementations • 20 Aug 2024 • Xinyu Liu, Ke Jin
In this paper, we have compiled a new benchmark, MTFinEval, focusing on the LLMs' basic knowledge of economics, which can always be used as a basis for judgment.
no code implementations • 20 Aug 2024 • Xinyu Liu, Jing Zhang, Kexin Zhang, Xu Liu, Lingling Li
Video Object Segmentation (VOS) presents several challenges, including object occlusion and fragmentation, the dis-appearance and re-appearance of objects, and tracking specific objects within crowded scenes.
1 code implementation • 13 Jul 2024 • Xinyu Liu, Wuyang Li, Yixuan Yuan
DiffRect first utilizes a Label Context Calibration Module (LCC) to calibrate the biased relationship between classes by learning the category-wise correlation in pseudo labels, then apply Latent Feature Rectification Module (LFR) on the latent space to formulate and align the pseudo label distributions of different levels via latent diffusion.
no code implementations • 8 Jul 2024 • Chenxin Li, Xinyu Liu, Cheng Wang, Yifan Liu, Weihao Yu, Jing Shao, Yixuan Yuan
To tackle these, we propose an innovative Modality-prompted Heterogeneous Graph for Omnimodal Learning (GTP-4o), which embeds the numerous disparate clinical modalities into a unified representation, completes the deficient embedding of missing modality and reformulates the cross-modal learning with a graph-based aggregation.
2 code implementations • 24 Jun 2024 • Henghui Ding, Chang Liu, Yunchao Wei, Nikhila Ravi, Shuting He, Song Bai, Philip Torr, Deshui Miao, Xin Li, Zhenyu He, YaoWei Wang, Ming-Hsuan Yang, Zhensong Xu, Jiangtao Yao, Chengjing Wu, Ting Liu, Luoqi Liu, Xinyu Liu, Jing Zhang, Kexin Zhang, Yuting Yang, Licheng Jiao, Shuyuan Yang, Mingqi Gao, Jingnan Luo, Jinyu Yang, Jungong Han, Feng Zheng, Bin Cao, Yisi Zhang, Xuanxu Lin, Xingjian He, Bo Zhao, Jing Liu, Feiyu Pan, Hao Fang, Xiankai Lu
Moreover, we provide a new motion expression guided video segmentation dataset MeViS to study the natural language-guided video understanding in complex environments.
no code implementations • 15 Jun 2024 • Ying Fu, Yu Li, ShaoDi You, Boxin Shi, Linwei Chen, Yunhao Zou, Zichun Wang, Yichen Li, Yuze Han, Yingkai Zhang, Jianan Wang, Qinglin Liu, Wei Yu, Xiaoqian Lv, Jianing Li, Shengping Zhang, Xiangyang Ji, Yuanpei Chen, Yuhan Zhang, Weihang Peng, Liwen Zhang, Zhe Xu, Dingyong Gou, Cong Li, Senyan Xu, Yunkang Zhang, Siyuan Jiang, Xiaoqiang Lu, Licheng Jiao, Fang Liu, Xu Liu, Lingling Li, Wenping Ma, Shuyuan Yang, Haiyang Xie, Jian Zhao, Shihua Huang, Peng Cheng, Xi Shen, Zheng Wang, Shuai An, Caizhi Zhu, Xuelong Li, Tao Zhang, Liang Li, Yu Liu, Chenggang Yan, Gengchen Zhang, Linyan Jiang, Bingyi Song, Zhuoyu An, Haibo Lei, Qing Luo, Jie Song, YuAn Liu, Haoyuan Zhang, Lingfeng Wang, Wei Chen, Aling Luo, Cheng Li, Jun Cao, Shu Chen, Zifei Dou, Xinyu Liu, Jing Zhang, Kexin Zhang, Yuting Yang, Xuejian Gou, Qinliang Wang, Yang Liu, Shizhan Zhao, Yanzhao Zhang, Libo Yan, Yuwei Guo, Guoxin Li, Qiong Gao, Chenyue Che, Long Sun, Xiang Chen, Hao Li, Jinshan Pan, Chuanlong Xie, Hongming Chen, Mingrui Li, Tianchen Deng, Jingwei Huang, Yufeng Li, Fei Wan, Bingxin Xu, Jian Cheng, Hongzhe Liu, Cheng Xu, Yuxiang Zou, Weiguo Pan, Songyin Dai, Sen Jia, Junpei Zhang, Puhua Chen, Qihang Li
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies.
no code implementations • 6 Jun 2024 • Xinyu Liu, Jing Zhang, Kexin Zhang, Yuting Yang, Licheng Jiao, Shuyuan Yang
Video Object Segmentation (VOS) is a vital task in computer vision, focusing on distinguishing foreground objects from the background across video frames.
2 code implementations • 5 Jun 2024 • Chenxin Li, Xinyu Liu, Wuyang Li, Cheng Wang, Hengyu Liu, Yifan Liu, Zhen Chen, Yixuan Yuan
We further delved into the potential of U-KAN as an alternative U-Net noise predictor in diffusion models, demonstrating its applicability in generating task-oriented model architectures.
no code implementations • 19 Apr 2024 • Xinyu Liu, Hai Zhang
Second, we reveal that there exists a fundamental limit to the problem of estimating the number of Gaussian components or model order in the mixture model if the number of i. i. d samples is finite.
no code implementations • 7 Apr 2024 • Zhiqiang Cai, Tong Ding, Min Liu, Xinyu Liu, Jianlin Xia
In this paper, we propose a structure-guided Gauss-Newton (SgGN) method for solving least squares problems using a shallow ReLU neural network.
no code implementations • CVPR 2024 • Jinlong Li, Baolu Li, Zhengzhong Tu, Xinyu Liu, Qing Guo, Felix Juefei-Xu, Runsheng Xu, Hongkai Yu
Vision-centric perception systems for autonomous driving have gained considerable attention recently due to their cost-effectiveness and scalability, especially compared to LiDAR-based systems.
1 code implementation • 28 Mar 2024 • Bu Jin, Yupeng Zheng, Pengfei Li, Weize Li, Yuhang Zheng, Sujie Hu, Xinyu Liu, Jinwei Zhu, Zhijie Yan, Haiyang Sun, Kun Zhan, Peng Jia, Xiaoxiao Long, Yilun Chen, Hao Zhao
However, the exploration of 3D dense captioning in outdoor scenes is hindered by two major challenges: 1) the domain gap between indoor and outdoor scenes, such as dynamics and sparse visual inputs, makes it difficult to directly adapt existing indoor methods; 2) the lack of data with comprehensive box-caption pair annotations specifically tailored for outdoor scenes.
no code implementations • 17 Mar 2024 • Chenxin Li, Hengyu Liu, Yifan Liu, Brandon Y. Feng, Wuyang Li, Xinyu Liu, Zhen Chen, Jing Shao, Yixuan Yuan
In a nutshell, Endora marks a notable breakthrough in the deployment of generative AI for clinical endoscopy research, setting a substantial stage for further advances in medical content generation.
no code implementations • 17 Mar 2024 • Baolu Li, Jinlong Li, Xinyu Liu, Runsheng Xu, Zhengzhong Tu, Jiacheng Guo, Xiaopeng Li, Hongkai Yu
In this paper, we propose a Domain Generalization based approach, named V2X-DGW, for LiDAR-based 3D object detection on multi-agent perception system under adverse weather conditions.
1 code implementation • 26 Feb 2024 • Zhen Chen, Qing Xu, Xinyu Liu, Yixuan Yuan
Moreover, to unleash the generalization capability of SAM across a variety of nuclei images, we devise a Domain-adaptive Tuning Encoder (DT-Encoder) to seamlessly harmonize visual features with domain-common and domain-specific knowledge, and further devise a Domain Query-enhanced Decoder (DQ-Decoder) by leveraging learnable domain queries for segmentation decoding in different nuclei domains.
1 code implementation • 6 Feb 2024 • Jinlong Li, Baolu Li, Xinyu Liu, Runsheng Xu, Jiaqi Ma, Hongkai Yu
However, the data source to train the various agents is independent and private in each company, leading to the Distribution Gap of different private data for training distinct agents in multi-agent perception system.
1 code implementation • 30 Jan 2024 • Jinlong Li, Baolu Li, Xinyu Liu, Jianwu Fang, Felix Juefei-Xu, Qing Guo, Hongkai Yu
The multi-agent perception system collects visual data from sensors located on various agents and leverages their relative poses determined by GPS signals to effectively fuse information, mitigating the limitations of single-agent sensing, such as occlusion.
2 code implementations • 19 Sep 2023 • Shaocong Xu, Pengfei Li, Qianpu Sun, Xinyu Liu, Yang Li, Shihui Guo, Zhen Wang, Bo Jiang, Rui Wang, Kehua Sheng, Bo Zhang, Li Jiang, Hao Zhao, Yilun Chen
We demonstrate that learning different abstaining penalties, apart from point-wise penalty, for different types of (synthesized) outliers can further improve the performance.
1 code implementation • 18 Jul 2023 • Jinlong Li, Runsheng Xu, Xinyu Liu, Jin Ma, Baolu Li, Qin Zou, Jiaqi Ma, Hongkai Yu
To bridge the domain gap and improve the performance of object detection in foggy and rainy weather, this paper presents a novel framework for domain-adaptive object detection.
no code implementations • 16 Jul 2023 • Jinlong Li, Runsheng Xu, Xinyu Liu, Baolu Li, Qin Zou, Jiaqi Ma, Hongkai Yu
We investigate the effects of these two types of domain gaps and propose a novel uncertainty-aware vision transformer to effectively relief the Deployment Gap and an agent-based feature adaptation module with inter-agent and ego-agent discriminators to reduce the Feature Gap.
no code implementations • 27 Jun 2023 • Chiori Hori, Puyuan Peng, David Harwath, Xinyu Liu, Kei Ota, Siddarth Jain, Radu Corcodel, Devesh Jha, Diego Romeres, Jonathan Le Roux
This paper introduces a method for robot action sequence generation from instruction videos using (1) an audio-visual Transformer that converts audio-visual features and instruction speech to a sequence of robot actions called dynamic movement primitives (DMPs) and (2) style-transfer-based training that employs multi-task learning with video captioning and weakly-supervised learning with a semantic classifier to exploit unpaired video-action data.
no code implementations • 26 Jun 2023 • Xinyu Liu, Jinlong Li, Jin Ma, Huiming Sun, Zhigang Xu, Tianyun Zhang, Hongkai Yu
To the best of our knowledge, this paper represents the first comprehensive survey on the topic of the deep transfer learning for intelligent vehicle perception.
no code implementations • 24 Jun 2023 • Xinyu Liu, Yan Ding, Kaikai An, Chunyang Xiao, Pranava Madhyastha, Tong Xiao, Jingbo Zhu
While state-of-the-art NLP models have demonstrated excellent performance for aspect based sentiment analysis (ABSA), substantial evidence has been presented on their lack of robustness.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
3 code implementations • CVPR 2023 • Xinyu Liu, Houwen Peng, Ningxin Zheng, Yuqing Yang, Han Hu, Yixuan Yuan
Comprehensive experiments demonstrate EfficientViT outperforms existing efficient models, striking a good trade-off between speed and accuracy.
no code implementations • 29 Apr 2023 • Zhenxiang Xiao, Yuzhong Chen, Lu Zhang, Junjie Yao, Zihao Wu, Xiaowei Yu, Yi Pan, Lin Zhao, Chong Ma, Xinyu Liu, Wei Liu, Xiang Li, Yixuan Yuan, Dinggang Shen, Dajiang Zhu, Tianming Liu, Xi Jiang
Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks.
1 code implementation • CVPR 2023 • Xinyu Liu, Beiwen Tian, Zhen Wang, Rui Wang, Kehua Sheng, Bo Zhang, Hao Zhao, Guyue Zhou
Thanks to the impressive progress of large-scale vision-language pretraining, recent recognition models can classify arbitrary objects in a zero-shot and open-set manner, with a surprisingly high accuracy.
1 code implementation • 1 Feb 2023 • Bu Jin, Xinyu Liu, Yupeng Zheng, Pengfei Li, Hao Zhao, Tong Zhang, Yuhang Zheng, Guyue Zhou, Jingjing Liu
To bridge the gap, we propose an end-to-end transformer-based architecture, ADAPT (Action-aware Driving cAPtion Transformer), which provides user-friendly natural language narrations and reasoning for each decision making step of autonomous vehicular control and action.
1 code implementation • 16 Dec 2022 • Jinlong Li, Runsheng Xu, Xinyu Liu, Jin Ma, Zicheng Chi, Jiaqi Ma, Hongkai Yu
Due to the beneficial Vehicle-to-Vehicle (V2V) communication, the deep learning based features from other agents can be shared to the ego vehicle so as to improve the perception of the ego vehicle.
no code implementations • 7 Dec 2022 • Yinpeng Dong, Peng Chen, Senyou Deng, Lianji L, Yi Sun, Hanyu Zhao, Jiaxing Li, Yunteng Tan, Xinyu Liu, Yangyi Dong, Enhui Xu, Jincai Xu, Shu Xu, Xuelin Fu, Changfeng Sun, Haoliang Han, Xuchong Zhang, Shen Chen, Zhimin Sun, Junyi Cao, Taiping Yao, Shouhong Ding, Yu Wu, Jian Lin, Tianpeng Wu, Ye Wang, Yu Fu, Lin Feng, Kangkang Gao, Zeyu Liu, Yuanzhe Pang, Chengqi Duan, Huipeng Zhou, Yajie Wang, Yuhang Zhao, Shangbo Wu, Haoran Lyu, Zhiyu Lin, YiFei Gao, Shuang Li, Haonan Wang, Jitao Sang, Chen Ma, Junhao Zheng, Yijia Li, Chao Shen, Chenhao Lin, Zhichao Cui, Guoshuai Liu, Huafeng Shi, Kun Hu, Mengxin Zhang
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems.
1 code implementation • CVPR 2022 • Xinyu Liu, Wuyang Li, Qiushi Yang, Baopu Li, Yixuan Yuan
Domain Adaptive Object Detection (DAOD) models a joint distribution of images and labels from an annotated source domain and learns a domain-invariant transformation to estimate the target labels with the given target domain images.
1 code implementation • CVPR 2022 • Wuyang Li, Xinyu Liu, Yixuan Yuan
To overcome these challenges, we propose a novel SemantIc-complete Graph MAtching (SIGMA) framework for DAOD, which completes mismatched semantics and reformulates the adaptation with graph matching.
no code implementations • 24 Jan 2022 • Mingzhe Chen, Xi Xiao, Bin Zhang, Xinyu Liu, Runiu Lu
In this paper, we propose to extend Neural Architecture Search (NAS) technique for designing an optimal model for multiple facial attributes-based depression recognition, which can be efficiently and robustly implemented in a small dataset.
1 code implementation • CVPR 2022 • Chenchen Jing, Yunde Jia, Yuwei Wu, Xinyu Liu, Qi Wu
Existing VQA models can answer a compositional question well, but cannot work well in terms of reasoning consistency in answering the compositional question and its sub-questions.
1 code implementation • 24 Oct 2021 • Xinyu Liu, Baopu Li, Zhen Chen, Yixuan Yuan
Model pruning aims to reduce the deep neural network (DNN) model size or computational overhead.
no code implementations • 11 Oct 2021 • Eric Hsiung, Hiloni Mehta, Junchi Chu, Xinyu Liu, Roma Patel, Stefanie Tellex, George Konidaris
We compare our method of mapping natural language task specifications to intermediate contextual queries against state-of-the-art CopyNet models capable of translating natural language to LTL, by evaluating whether correct LTL for manipulation and navigation task specifications can be output, and show that our method outperforms the CopyNet model on unseen object references.
1 code implementation • 7 Sep 2021 • Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng, Michele Donini, Jason Gelman, Satish Gollaprolu, John He, Pedro Larroy, Xinyu Liu, Nick McCarthy, Ashish Rathi, Scott Rees, Ankit Siva, ErhYuan Tsai, Keerthan Vasist, Pinar Yilmaz, Muhammad Bilal Zafar, Sanjiv Das, Kevin Haas, Tyler Hill, Krishnaram Kenthapadi
We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into data and ML models by identifying biases and explaining predictions.
no code implementations • 8 Jul 2021 • Haibo Qi, YuHan Wang, Xinyu Liu
In this paper, a 3D-RegNet-based neural network is proposed for diagnosing the physical condition of patients with coronavirus (Covid-19) infection.
1 code implementation • ACL 2020 • Raphael Tang, Jaejun Lee, Ji Xin, Xinyu Liu, Yao-Liang Yu, Jimmy Lin
In natural language processing, a recently popular line of work explores how to best report the experimental results of neural networks.
no code implementations • 22 Mar 2020 • Xinyu Liu, Xiren Miao, Hao Jiang, Jing Chen
With the aim of providing a comprehensive overview for researchers who are interested in developing a deep-learning-based analysis system for power lines inspection data, this paper conducts a thorough review of the current literature and identifies the challenges for future research.
no code implementations • 22 Mar 2020 • Xinyu Liu, Xiaoguang Di
Lightweight or mobile neural networks used for real-time computer vision tasks contain fewer parameters than normal networks, which lead to a constrained performance.
1 code implementation • 5 Nov 2019 • Zhiqiang Cai, Jingshuang Chen, Min Liu, Xinyu Liu
This paper studies an unsupervised deep learning-based numerical approach for solving partial differential equations (PDEs).
no code implementations • SEMEVAL 2019 • Jiahui Han, Shengtan Wu, Xinyu Liu
In this paper, we present two methods to identify and categorize the offensive language in Twitter.
no code implementations • 31 Jan 2019 • Ya Li, Xinyu Liu, Dan Liu, Xueqiang Zhang, Junhua Liu
Recent years has witnessed dramatic progress of neural machine translation (NMT), however, the method of manually guiding the translation procedure remains to be better explored.
no code implementations • 1 Nov 2018 • Hao Li, Yang Wang, Xinyu Liu, Zhichao Sheng, Si Wei
We propose a nested recurrent neural network (nested RNN) model for English spelling error correction and generate pseudo data based on phonetic similarity to train it.
no code implementations • 19 Sep 2017 • Jeffrey Mahler, Matthew Matl, Xinyu Liu, Albert Li, David Gealy, Ken Goldberg
Vacuum-based end effectors are widely used in industry and are often preferred over parallel-jaw and multifinger grippers due to their ability to lift objects with a single point of contact.
Robotics
no code implementations • 27 Mar 2017 • Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Aparicio Ojea, Ken Goldberg
To reduce data collection time for deep learning of robust robotic grasp plans, we explore training from a synthetic dataset of 6. 7 million point clouds, grasps, and analytic grasp metrics generated from thousands of 3D models from Dex-Net 1. 0 in randomized poses on a table.
Robotics