no code implementations • 5 Jun 2024 • Peijie Dong, Lujun Li, Zhenheng Tang, Xiang Liu, Xinglin Pan, Qiang Wang, Xiaowen Chu
In particular, we devise an elaborate search space encompassing the existing pruning metrics to discover the potential symbolic pruning metric.
no code implementations • 29 May 2024 • Zhenbei Wu, Qiang Wang, Jie Yang
There continues to be a lack of large-scale paired datasets for scene sketches.
no code implementations • 27 May 2024 • Qiang Wang, Minghua Liu, Junjun Hu, Fan Jiang, Mu Xu
In contrast, we propose an efficient long-duration video generation method based on noise reschedule specifically tailored for image animation tasks, facilitating the creation of videos over 100 frames in length while maintaining consistency in content scenery and motion coordination.
1 code implementation • 25 Mar 2024 • Xiaoxuan Yu, Hao Wang, Weiming Li, Qiang Wang, SoonYong Cho, Younghun Sung
In this work, we propose a novel Disentangled Object-Centric TRansformer (DOCTR) that explores object-centric representation to facilitate learning with multiple objects for the multiple sub-tasks in a unified manner.
no code implementations • 24 Mar 2024 • Shiben Liu, Huijie Fan, Qiang Wang, Xiai Chen, Zhi Han, Yandong Tang
KU strategy enhances the adaptive learning ability of learner models for new information under the adjustment model prior, and KP strategy preserves old knowledge operated by representation-level alignment and logit-level supervision in limited old task datasets while guaranteeing the adaptive learning information capacity of the LReID model.
no code implementations • 20 Mar 2024 • Yamin Mao, Zhihua Liu, Weiming Li, SoonYong Cho, Qiang Wang, Xiaoshuai Hao
Recently, dense regression methods have attracted increasing attention in 3D hand pose estimation task, which provide a low computational burden and high accuracy regression way by densely regressing hand joint offset maps.
1 code implementation • 14 Feb 2024 • Qiang Wang, Yixin Deng, Francisco Roldan Sanchez, Keru Wang, Kevin McGuinness, Noel O'Connor, Stephen J. Redmond
Offline policy learning aims to discover decision-making policies from previously-collected datasets without additional online interactions with the environment.
no code implementations • 3 Feb 2024 • Peijie Dong, Lujun Li, Xinglin Pan, Zimian Wei, Xiang Liu, Qiang Wang, Xiaowen Chu
Recent advancements in Zero-shot Neural Architecture Search (NAS) highlight the efficacy of zero-cost proxies in various NAS benchmarks.
1 code implementation • 19 Jan 2024 • Chao Pang, Xingxing Weng, Jiang Wu, Qiang Wang, Gui-Song Xia
This ensures effective knowledge transfer while maintaining the student model's training flexibility.
no code implementations • 14 Dec 2023 • Yi Xin, Junlong Du, Qiang Wang, Ke Yan, Shouhong Ding
On the one hand, to maximize the complementarity of tasks with high similarity, we utilize a gradient-driven task grouping method that partitions tasks into several disjoint groups and assign a group-shared MmAP to each group.
no code implementations • 14 Dec 2023 • Yi Xin, Junlong Du, Qiang Wang, Zhiwen Lin, Ke Yan
Extensive experiments on four dense scene understanding tasks demonstrate the superiority of VMT-Adapter(-Lite), achieving a 3. 96%(1. 34%) relative improvement compared to single-task full fine-tuning, while utilizing merely ~1% (0. 36%) trainable parameters of the pre-trained model.
no code implementations • 14 Dec 2023 • Qingsong Yan, Qiang Wang, Kaiyong Zhao, Jie Chen, Bo Li, Xiaowen Chu, Fei Deng
Neural Radiance Fields (NeRF) have demonstrated impressive performance in novel view synthesis.
1 code implementation • 3 Oct 2023 • Francisco Roldan Sanchez, Qiang Wang, David Cordova Bulens, Kevin McGuinness, Stephen Redmond, Noel O'Connor
Hindsight Experience Replay (HER) is a technique used in reinforcement learning (RL) that has proven to be very efficient for training off-policy RL-based agents to solve goal-based robotic manipulation tasks using sparse rewards.
no code implementations • 25 Sep 2023 • Xiongfeng Peng, Zhihua Liu, Weiming Li, Ping Tan, SoonYong Cho, Qiang Wang
Recent deep learning based visual simultaneous localization and mapping (SLAM) methods have made significant progress.
no code implementations • 3 Sep 2023 • Zhenheng Tang, Yuxin Wang, Xin He, Longteng Zhang, Xinglin Pan, Qiang Wang, Rongfei Zeng, Kaiyong Zhao, Shaohuai Shi, Bingsheng He, Xiaowen Chu
The rapid growth of memory and computation requirements of large language models (LLMs) has outpaced the development of hardware, hindering people who lack large-scale high-end GPUs from training or deploying LLMs.
1 code implementation • 28 Aug 2023 • Yang Liu, Cheng Yu, Lei Shang, Yongyi He, Ziheng Wu, Xingjun Wang, Chao Xu, Haoyu Xie, Weida Wang, Yuze Zhao, Lin Zhu, Chen Cheng, Weitao Chen, Yuan YAO, Wenmeng Zhou, Jiaqi Xu, Qiang Wang, Yingda Chen, Xuansong Xie, Baigui Sun
In this paper, we present FaceChain, a personalized portrait generation framework that combines a series of customized image-generation model and a rich set of face-related perceptual understanding models (\eg, face detection, deep face embedding extraction, and facial attribute recognition), to tackle aforementioned challenges and to generate truthful personalized portraits, with only a handful of portrait images as input.
1 code implementation • 25 Aug 2023 • Jiawei Liu, Qiang Wang, Huijie Fan, Yinong Wang, Yandong Tang, Liangqiong Qu
We propose residual denoising diffusion models (RDDM), a novel dual diffusion process that decouples the traditional single denoising diffusion process into residual diffusion and noise diffusion.
no code implementations • 15 Aug 2023 • Nico Gürtler, Felix Widmaier, Cansu Sancaktar, Sebastian Blaes, Pavel Kolev, Stefan Bauer, Manuel Wüthrich, Markus Wulfmeier, Martin Riedmiller, Arthur Allshire, Qiang Wang, Robert McCarthy, Hangyeol Kim, Jongchan Baek, Wookyong Kwon, Shanliang Qian, Yasunori Toshimitsu, Mike Yan Michelis, Amirhossein Kazemipour, Arman Raayatsanati, Hehui Zheng, Barnabas Gavin Cangan, Bernhard Schölkopf, Georg Martius
For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and benchmark algorithms.
no code implementations • 9 Aug 2023 • Qiang Wang, Junlong Du, Ke Yan, Shouhong Ding
We propose that the key lies in explicitly modeling the motion cues flowing in video frames.
no code implementations • 7 Aug 2023 • Ruiqi Zhang, Jie Chen, Qiang Wang
This paper proposes a technique for efficiently modeling dynamic humans by explicifying the implicit neural fields via a Neural Explicit Surface (NES).
1 code implementation • IEEE Transactions on Neural Networks and Learning Systems 2023 • Jiawei Liu, Qiang Wang, Huijie Fan, Jiandong Tian, Yandong Tang
Thus, our network ensures the fidelity of nonshadow areas and restores the light intensity of shadow areas through three-branch collaboration.
no code implementations • 8 Jul 2023 • Qiang Wang, Pablo Martinez Ulloa, Robert Burke, David Cordova Bulens, Stephen J. Redmond
When transferring the trained model to a robotic gripping environment distinct from where the training data was collected, our model maintained robust performance, with a success rate of 96. 8%, providing timely feedback for stabilizing several practical gripping tasks.
no code implementations • 30 Jun 2023 • Yan Gao, Yan Wang, Qiang Wang
However, in time series forecasting, it is difficult to obtain enough data, which limits the performance of neural forecasting models.
1 code implementation • 27 Jun 2023 • Xue-Feng Zhu, Tianyang Xu, Jian Zhao, Jia-Wei Liu, Kai Wang, Gang Wang, Jianan Li, Qiang Wang, Lei Jin, Zheng Zhu, Junliang Xing, Xiao-Jun Wu
Still, previous works have simplified such an anti-UAV task as a tracking problem, where the prior information of UAVs is always provided; such a scheme fails in real-world anti-UAV tasks (i. e. complex scenes, indeterminate-appear and -reappear UAVs, and real-time UAV surveillance).
1 code implementation • ICCV 2023 • Xuesong Chen, Shaoshuai Shi, Chao Zhang, Benjin Zhu, Qiang Wang, Ka Chun Cheung, Simon See, Hongsheng Li
3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots.
no code implementations • 8 Jun 2023 • Zhaoyang Huang, Xiaoyu Shi, Chao Zhang, Qiang Wang, Yijin Li, Hongwei Qin, Jifeng Dai, Xiaogang Wang, Hongsheng Li
This paper introduces a novel transformer-based network architecture, FlowFormer, along with the Masked Cost Volume AutoEncoding (MCVA) for pretraining it to tackle the problem of optical flow estimation.
1 code implementation • 30 May 2023 • Qiang Wang, Di Kong, Fengyin Lin, Yonggang Qi
Creative sketch is a universal way of visual expression, but translating images from an abstract sketch is very challenging.
no code implementations • 20 Mar 2023 • Rongxiang Weng, Qiang Wang, Wensen Cheng, Changfeng Zhu, Min Zhang
A contributing factor to this problem is that NMT models trained with the one-to-one paradigm struggle to handle the source diversity phenomenon, where inputs with the same meaning can be expressed differently.
no code implementations • 16 Mar 2023 • Shuhan Qi, Shuhao Zhang, Qiang Wang, Jiajia Zhang, Jing Xiao, Xuan Wang
In this paper, we propose a scalable value-decomposition exploration (SVDE) method, which includes a scalable training mechanism, intrinsic reward design, and explorative experience replay.
Multi-agent Reinforcement Learning reinforcement-learning +3
1 code implementation • IEEE Transactions on Multimedia 2023 • Jiawei Liu, Qiang Wang, Huijie Fan, Wentao Li, Liangqiong Qu, Yandong Tang
Last, these features are converted to a target shadow-free image, affiliated shadow matte, and shadow image, supervised by multi-task joint loss functions.
1 code implementation • 30 Jan 2023 • Qiang Wang, Robert McCarthy, David Cordova Bulens, Francisco Roldan Sanchez, Kevin McGuinness, Noel E. O'Connor, Stephen J. Redmond
However, BC's performance deteriorated when applied to mixed datasets, and the performance of offline RL algorithms was also unsatisfactory.
no code implementations • 27 Jan 2023 • Qiang Wang, Robert McCarthy, David Cordova Bulens, Kevin McGuinness, Noel E. O'Connor, Nico Gürtler, Felix Widmaier, Francisco Roldan Sanchez, Stephen J. Redmond
Learning control policies offline from pre-recorded datasets is a promising avenue for solving challenging real-world problems.
no code implementations • 3 Dec 2022 • Bo Qin, Aixin Jia, Qiang Wang, Jianning Lu, Shuqin Pan, Haibo Wang, Ming Chen
This paper describes the submission of the RoyalFlush neural machine translation system for the WMT 2022 translation efficiency task.
1 code implementation • 30 Nov 2022 • Qingsong Yan, Qiang Wang, Kaiyong Zhao, Bo Li, Xiaowen Chu, Fei Deng
Existing learning-based multi-view stereo (MVS) methods rely on the depth range to build the 3D cost volume and may fail when the range is too large or unreliable.
no code implementations • 19 Oct 2022 • Qiang Wang, Xinhui Hu, Ming Chen
HRT achieves the state-of-the-art BLEU score of 28. 49 on the WMT En-De task and is at least 1. 5x faster than AT, regardless of batch size and device.
1 code implementation • Molecular Omics 2022 • Jiaojiao Zhao, Haoqiang Jiang, Guoyang Zou, Qian Lin, Qiang Wang, Jia Liu, Leina Ma
The deep-learning model CNN combined with the One-Hot coding showed the best performance, dubbed CNNArginineMe.
1 code implementation • Conference 2022 • Jin Li, Zhong Ji, Gang Wang, Qiang Wang, Feng Gao
The goal of General Continual Learning (GCL) is to preserve learned knowledge and learn new knowledge with constant memory from an infinite data stream where task boundaries are blurry.
no code implementations • COLING 2022 • Qiang Wang, Rongxiang Weng, Ming Chen
Generally, kNN-MT borrows the off-the-shelf context representation in the translation task, e. g., the output of the last decoder layer, as the query vector of the retrieval task.
no code implementations • 29 Aug 2022 • Qingsong Yan, Qiang Wang, Kaiyong Zhao, Bo Li, Xiaowen Chu, Fei Deng
The panorama image can simultaneously demonstrate complete information of the surrounding environment and has many advantages in virtual tourism, games, robotics, etc.
Ranked #17 on Depth Estimation on Stanford2D3D Panoramic
no code implementations • 9 Aug 2022 • Han Ji, Qiang Wang, Stephen J. Redmond, Iman Tavakkolnia, Xiping Wu
In this paper, a novel deep neural network (DNN) structure named adaptive target-condition neural network (A-TCNN) is proposed, which conducts AP selection for one target user upon the condition of other users.
1 code implementation • 20 Jul 2022 • Qiang Wang, Shaohuai Shi, Kaiyong Zhao, Xiaowen Chu
However, existing NAS studies on the dense prediction task, especially stereo matching, still cannot be efficiently and effectively deployed on devices of different computing capabilities.
no code implementations • 20 Jul 2022 • Leiyang Xu, Qiang Wang, Xiaotian Lin, Lin Yuan
This study proposes an efficient framework for the few-shot skeleton-based TAS, including a data augmentation method and an improved model.
no code implementations • 16 Jul 2022 • Xiaotian Lin, Leiyang Xu, Qiang Wang
In the past decade, object detection tasks are defined mostly by large public datasets.
no code implementations • 5 Jul 2022 • Weiming Hu, Qiang Wang, Li Zhang, Luca Bertinetto, Philip H. S. Torr
In this paper we introduce SiamMask, a framework to perform both visual object tracking and video object segmentation, in real-time, with the same simple method.
no code implementations • 30 Jun 2022 • Lin Yuan, Zhen He, Qiang Wang, Leiyang Xu, Xiang Ma
Human action recognition is a quite hugely investigated area where most remarkable action recognition networks usually use large-scale coarse-grained action datasets of daily human actions as inputs to state the superiority of their networks.
1 code implementation • 26 Jun 2022 • Lingzhi Qi, Xixi Li, Qiang Wang, Suling Jia
We evaluate our approach on the widely used forecasting competition data set M4, in terms of both point forecasts and prediction intervals.
1 code implementation • 19 May 2022 • Qiang Wang, Francisco Roldan Sanchez, Robert McCarthy, David Cordova Bulens, Kevin McGuinness, Noel O'Connor, Manuel Wüthrich, Felix Widmaier, Stefan Bauer, Stephen J. Redmond
Here we extend this method, by modifying the task of Phase 1 of the RRC to require the robot to maintain the cube in a particular orientation, while the cube is moved along the required positional trajectory.
1 code implementation • 30 Mar 2022 • Zhaoyang Huang, Xiaoyu Shi, Chao Zhang, Qiang Wang, Ka Chun Cheung, Hongwei Qin, Jifeng Dai, Hongsheng Li
We introduce optical Flow transFormer, dubbed as FlowFormer, a transformer-based neural network architecture for learning optical flow.
Ranked #1 on Optical Flow Estimation on Sintel-final
no code implementations • CVPR 2022 • Yingjie Cai, Kwan-Yee Lin, Chao Zhang, Qiang Wang, Xiaogang Wang, Hongsheng Li
Specifically, we map a series of related partial point clouds into multiple complete shape and occlusion code pairs and fuse the codes to obtain their representations in the unified latent space.
no code implementations • CVPR 2022 • Qiang Wang, Yanhao Zhang, Yun Zheng, Pan Pan
Temporal representation is the cornerstone of modern action detection techniques.
2 code implementations • 14 Mar 2022 • Qiang Wang, Yanhao Zhang, Yun Zheng, Pan Pan, Xian-Sheng Hua
Cross-modality interaction is a critical component in Text-Video Retrieval (TVR), yet there has been little examination of how different influencing factors for computing interaction affect performance.
Ranked #10 on Video Retrieval on MSR-VTT-1kA (using extra training data)
1 code implementation • 15 Feb 2022 • Qiang Wang, Susan Fernandes, Gareth O. S. Williams, Neil Finlayson, Ahsan R. Akram, Kevin Dhaliwal, James R. Hopgood, Marta Vallejo
Autofluorescence lifetime images reveal unique characteristics of endogenous fluorescence in biological samples.
Medical Image Registration Unsupervised Image-To-Image Translation
no code implementations • 27 Oct 2021 • Haojin Liao, Xiaolin Song, Sicheng Zhao, Shanghang Zhang, Xiangyu Yue, Xingxu Yao, Yueming Zhang, Tengfei Xing, Pengfei Xu, Qiang Wang
The Visual Domain Adaptation (VisDA) 2021 Challenge calls for unsupervised domain adaptation (UDA) methods that can deal with both input distribution shift and label set variance between the source and target domains.
no code implementations • 6 Oct 2021 • Qiang Wang, Shaohuai Shi, Shizhen Zheng, Kaiyong Zhao, Xiaowen Chu
The disparity estimation problem tends to be addressed by DNNs which achieve much better prediction accuracy than traditional hand-crafted feature-based methods.
no code implementations • 5 Oct 2021 • Robert McCarthy, Qiang Wang, Stephen J. Redmond
Model-based reinforcement learning is a promising learning strategy for practical robotic applications due to its improved data-efficiency versus model-free counterparts.
2 code implementations • 30 Sep 2021 • Robert McCarthy, Francisco Roldan Sanchez, Qiang Wang, David Cordova Bulens, Kevin McGuinness, Noel O'Connor, Stephen J. Redmond
This paper details our winning submission to Phase 1 of the 2021 Real Robot Challenge; a challenge in which a three-fingered robot must carry a cube along specified goal trajectories.
1 code implementation • 3 Sep 2021 • Zhong Ji, Jin Li, Qiang Wang, Zhongfei Zhang
Furthermore, we explore a collaborative self-supervision idea to leverage pretext tasks and supervised contrastive learning for addressing the feature deviation problem by learning complete and discriminative features for all classes.
no code implementations • 17 Jun 2021 • Wenpeng Xing, Jie Chen, Zaifeng Yang, Qiang Wang
Image-based geometric modeling and novel view synthesis based on sparse, large-baseline samplings are challenging but important tasks for emerging multimedia applications such as virtual reality and immersive telepresence.
no code implementations • 29 Apr 2021 • Gaigai Tang, Lianxiao Meng, Shuangyin Ren, Weipeng Cao, Qiang Wang, Lin Yang
To solve this problem, we have conducted extensive experiments to test the performance of the two most typical neural networks (i. e., Bi-LSTM and RVFL) with the two most classical data preprocessing methods (i. e., the vector representation and the program symbolization methods) on software vulnerability detection problems and obtained a series of interesting research conclusions, which can provide valuable guidelines for researchers and engineers.
no code implementations • CVPR 2021 • Qiang Wang, Yun Zheng, Pan Pan, Yinghui Xu
Recent works have shown that convolutional networks have substantially improved the performance of multiple object tracking by simultaneously learning detection and appearance features.
no code implementations • 3 Mar 2021 • Chao Zhang, Wenqiang Yi, Yuanwei Liu, Qiang Wang
Numerical results indicate that 1) although the interference from other cells is enhanced via the RISs, the performance of the RIS-aided user still enhances since the channel quality is strengthened more obviously; and 2) the SIC order can be altered by employing the RISs since the RISs improve the channel quality of the aided user.
Information Theory Information Theory
no code implementations • 26 Feb 2021 • Xiongfeng Peng, Zhihua Liu, Qiang Wang, Yun-Tae Kim, Myungjae Jeon
We propose a novel feature re-identification method for real-time visual-inertial SLAM.
no code implementations • 9 Feb 2021 • Yanhao Zhang, Qiang Wang, Pan Pan, Yun Zheng, Cheng Da, Siyang Sun, Yinghui Xu
Nowadays, live-stream and short video shopping in E-commerce have grown exponentially.
1 code implementation • 21 Jan 2021 • Nan Jiang, Kuiran Wang, Xiaoke Peng, Xuehui Yu, Qiang Wang, Junliang Xing, Guorong Li, Jian Zhao, Guodong Guo, Zhenjun Han
The releasing of such a large-scale dataset could be a useful initial step in research of tracking UAVs.
no code implementations • ICCV 2021 • Yamin Mao, Zhihua Liu, Weiming Li, Yuchao Dai, Qiang Wang, Yun-Tae Kim, Hong-Seok Lee
Extensive experiments show that the proposed method achieves the highest ground truth covering ratio compared with other cascade cost volume based stereo matching methods.
no code implementations • 1 Jan 2021 • Shaohui Kuang, Heng Yu, Weihua Luo, Qiang Wang
Existing ways either employ extra encoder to encode information from TM or concatenate source sentence and TM sentences as encoder's input.
no code implementations • COLING 2020 • Qiang Wang, Changliang Li, Yue Zhang, Tong Xiao, Jingbo Zhu
In this way, in addition to the topmost encoder layer (referred to as the primary view), we also incorporate an intermediate encoder layer as the auxiliary view.
no code implementations • CVPR 2021 • Songyan Zhang, Zhicheng Wang, Qiang Wang, Jinshuo Zhang, Gang Wei, Xiaowen Chu
Existing state-of-the-art disparity estimation works mostly leverage the 4D concatenation volume and construct a very deep 3D convolution neural network (CNN) for disparity regression, which is inefficient due to the high memory consumption and slow inference speed.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Qiang Wang, Tong Xiao, Jingbo Zhu
The standard neural machine translation model can only decode with the same depth configuration as training.
1 code implementation • 5 Oct 2020 • Jiawei Liu, Huijie Fan, Qiang Wang, Wentao Li, Yandong Tang, Danbo Wang, Mingyi Zhou, Li Chen
The qualitative and quantitative experimental results show that our LLPC can improve the quality of manual labels and the accuracy of overlapping cell edge detection.
1 code implementation • CVPR 2020 • Xiawu Zheng, Rongrong Ji, Qiang Wang, Qixiang Ye, Zhenguo Li, Yonghong Tian, Qi Tian
In this paper, we provide a novel yet systematic rethinking of PE in a resource constrained regime, termed budgeted PE (BPE), which precisely and effectively estimates the performance of an architecture sampled from an architecture space.
no code implementations • 22 Apr 2020 • Yikang Zhang, Jian Zhang, Qiang Wang, Zhao Zhong
On one hand, we can reduce the computation cost remarkably while maintaining the performance.
1 code implementation • 21 Apr 2020 • Zuling Chang, Martianus Frederic Ezerman, Adamas Aqsa Fahreza, Qiang Wang
Using greedy algorithms to generate de Bruijn sequences is a classical approach that has produced numerous interesting theoretical results.
Information Theory Combinatorics Information Theory
no code implementations • 19 Apr 2020 • Gan Sun, Yang Cong, Jiahua Dong, Qiang Wang, Ji Liu
To the end, experimental results on real-world datasets show that federated multi-task learning model is very sensitive to poisoning attacks, when the attackers either directly poison the target nodes or indirectly poison the related nodes by exploiting the communication protocol.
2 code implementations • 24 Mar 2020 • Qiang Wang, Shaohuai Shi, Shizhen Zheng, Kaiyong Zhao, Xiaowen Chu
Deep neural networks (DNNs) have achieved great success in the area of computer vision.
no code implementations • 24 Feb 2020 • Qiang Wang, Shaohuai Shi, Canhui Wang, Xiaowen Chu
We thus propose a provable algorithm, AdaDUAL, to efficiently schedule those communication tasks.
1 code implementation • COLING 2018 • Qiang Wang, Fuxue Li, Tong Xiao, Yanyang Li, Yinqiao Li, Jingbo Zhu
In this paper, we propose a multi-layer representation fusion (MLRF) approach to fusing stacked layers.
1 code implementation • 16 Feb 2020 • Yanyang Li, Qiang Wang, Tong Xiao, Tongran Liu, Jingbo Zhu
Though early successes of Statistical Machine Translation (SMT) systems are attributed in part to the explicit modelling of the interaction between any two source and target units, e. g., alignment, the recent Neural Machine Translation (NMT) systems resort to the attention which partially encodes the interaction for efficiency.
no code implementations • 5 Feb 2020 • Ruoshi Wen, Kai Yuan, Qiang Wang, Shuai Heng, Zhibin Li
Regulating contact forces with high precision is crucial for grasping and manipulating fragile or deformable objects.
Robotics
no code implementations • ICLR 2020 • Xin-Yu Zhang, Qiang Wang, Jian Zhang, Zhao Zhong
The augmentation policy network attempts to increase the training loss of a target network through generating adversarial augmentation policies, while the target network can learn more robust features from harder examples to improve the generalization.
Ranked #595 on Image Classification on ImageNet
no code implementations • 24 Dec 2019 • Muyuan Fang, Qiang Wang, Zhao Zhong
Automatic neural architecture search techniques are becoming increasingly important in machine learning area.
1 code implementation • 20 Dec 2019 • Qiang Wang, Shizhen Zheng, Qingsong Yan, Fei Deng, Kaiyong Zhao, Xiaowen Chu
Besides, we present DTN-Net, a two-stage deep model for surface normal estimation.
no code implementations • 20 Nov 2019 • Shaohuai Shi, Zhenheng Tang, Qiang Wang, Kaiyong Zhao, Xiaowen Chu
To reduce the long training time of large deep neural network (DNN) models, distributed synchronous stochastic gradient descent (S-SGD) is commonly used on a cluster of workers.
1 code implementation • ICCV 2019 • Zhao Yang, Qiang Wang, Luca Bertinetto, Weiming Hu, Song Bai, Philip H. S. Torr
Unsupervised video object segmentation has often been tackled by methods based on recurrent neural networks and optical flow.
Ranked #20 on Unsupervised Video Object Segmentation on DAVIS 2016 val
no code implementations • 25 Sep 2019 • Kane Zhang, Jian Zhang, Qiang Wang, Zhao Zhong
To verify the scalability, we also apply DyNet on segmentation task, the results show that DyNet can reduces 69. 3% FLOPs while maintaining the Mean IoU on segmentation task.
no code implementations • 15 Sep 2019 • Yuxin Wang, Qiang Wang, Shaohuai Shi, Xin He, Zhenheng Tang, Kaiyong Zhao, Xiaowen Chu
Different from the existing end-to-end benchmarks which only present the training time, We try to investigate the impact of hardware, vendor's software library, and deep learning framework on the performance and energy consumption of AI training.
no code implementations • WS 2019 • Bei Li, Yinqiao Li, Chen Xu, Ye Lin, Jiqiang Liu, Hui Liu, Ziyang Wang, Yuhao Zhang, Nuo Xu, Zeyang Wang, Kai Feng, Hexuan Chen, Tengbo Liu, Yanyang Li, Qiang Wang, Tong Xiao, Jingbo Zhu
We participated in 13 translation directions, including 11 supervised tasks, namely EN↔{ZH, DE, RU, KK, LT}, GU→EN and the unsupervised DE↔CS sub-track.
no code implementations • WS 2019 • Ruobing Li, Chuan Wang, Yefei Zha, Yonghong Yu, Shiman Guo, Qiang Wang, Yang Liu, Hui Lin
In this paper, we describe two systems we developed for the three tracks we have participated in the BEA-2019 GEC Shared Task.
Ranked #17 on Grammatical Error Correction on BEA-2019 (test)
2 code implementations • ACL 2019 • Qiang Wang, Bei Li, Tong Xiao, Jingbo Zhu, Changliang Li, Derek F. Wong, Lidia S. Chao
Transformer is the state-of-the-art model in recent machine translation evaluations.
1 code implementation • 14 Jan 2019 • Shaohuai Shi, Qiang Wang, Kaiyong Zhao, Zhenheng Tang, Yuxin Wang, Xiang Huang, Xiaowen Chu
Current methods that use AllGather to accumulate the sparse gradients have a communication complexity of $O(kP)$, where $P$ is the number of workers, which is inefficient on low bandwidth networks with a large number of workers.
1 code implementation • 2 Jan 2019 • Wei Chen, Jincai Chen, Fuhao Zou, Yuan-Fang Li, Ping Lu, Qiang Wang, Wei Zhao
The inverted index structure is amenable to GPU-based implementations, and the state-of-the-art systems such as Faiss are able to exploit the massive parallelism offered by GPUs.
13 code implementations • CVPR 2019 • Bo Li, Wei Wu, Qiang Wang, Fangyi Zhang, Junliang Xing, Junjie Yan
Moreover, we propose a new model architecture to perform depth-wise and layer-wise aggregations, which not only further improves the accuracy but also reduces the model size.
Ranked #5 on Visual Object Tracking on VOT2017/18
3 code implementations • CVPR 2019 • Qiang Wang, Li Zhang, Luca Bertinetto, Weiming Hu, Philip H. S. Torr
In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach.
Ranked #3 on Visual Object Tracking on YouTube-VOS 2018
no code implementations • WS 2018 • Qiang Wang, Bei Li, Jiqiang Liu, Bojian Jiang, Zheyang Zhang, Yinqiao Li, Ye Lin, Tong Xiao, Jingbo Zhu
This paper describes the submission of the NiuTrans neural machine translation system for the WMT 2018 Chinese ↔ English news translation tasks.
no code implementations • ECCV 2018 • Mengdan Zhang, Qiang Wang, Junliang Xing, Jin Gao, Peixi Peng, Weiming Hu, Steve Maybank
Correlation filters based trackers rely on a periodic assumption of the search sample to efficiently distinguish the target from the background.
1 code implementation • ECCV 2018 • Zheng Zhu, Qiang Wang, Bo Li, Wei Wu, Junjie Yan, Weiming Hu
During the off-line training phase, an effective sampling strategy is introduced to control this distribution and make the model focus on the semantic distractors.
Ranked #11 on Visual Object Tracking on VOT2017/18
no code implementations • ACL 2018 • Yanyang Li, Tong Xiao, Yinqiao Li, Qiang Wang, Changming Xu, Jingbo Zhu
We offer a simple and effective method to seek a better balance between model confidence and length preference for Neural Machine Translation (NMT).
2 code implementations • CVPR 2018 • Qiang Wang, Zhu Teng, Junliang Xing, Jin Gao, Weiming Hu, Stephen Maybank
The RASNet model reformulates the correlation filter within a Siamese tracking framework, and introduces different kinds of the attention mechanisms to adapt the model without updating the model online.
Ranked #3 on Visual Object Tracking on OTB-2013
no code implementations • ICCV 2017 • Zhu Teng, Junliang Xing, Qiang Wang, Congyan Lang, Songhe Feng, Yi Jin
Our deep architecture contains three networks, a Feature Net, a Temporal Net, and a Spatial Net.
5 code implementations • 13 Apr 2017 • Qiang Wang, Jin Gao, Junliang Xing, Mengdan Zhang, Weiming Hu
In this work, we present an end-to-end lightweight network architecture, namely DCFNet, to learn the convolutional features and perform the correlation tracking process simultaneously.
no code implementations • 25 Aug 2016 • Shaohuai Shi, Qiang Wang, Pengfei Xu, Xiaowen Chu
We first benchmark the running performance of these tools with three popular types of neural networks on two CPU platforms and three GPU platforms.
no code implementations • 16 Jul 2016 • Ke Yang, Dongsheng Li, Yong Dou, Shaohe Lv, Qiang Wang
Object detection is an import task of computer vision. A variety of methods have been proposed, but methods using the weak labels still do not have a satisfactory result. In this paper, we propose a new framework that using the weakly supervised method's output as the pseudo-strong labels to train a strongly supervised model. One weakly supervised method is treated as black-box to generate class-specific bounding boxes on train dataset. A de-noise method is then applied to the noisy bounding boxes. Then the de-noised pseudo-strong labels are used to train a strongly object detection network. The whole framework is still weakly supervised because the entire process only uses the image-level labels. The experiment results on PASCAL VOC 2007 prove the validity of our framework, and we get result 43. 4% on mean average precision compared to 39. 5% of the previous best result and 34. 5% of the initial method, respectively. And this frame work is simple and distinct, and is promising to be applied to other method easily.