no code implementations • 3 May 2023 • Tao Chen, Liang Lv, Di Wang, Jing Zhang, Yue Yang, Zeyang Zhao, Chen Wang, Xiaowei Guo, Hao Chen, Qingye Wang, Yufei Xu, Qiming Zhang, Bo Du, Liangpei Zhang, DaCheng Tao
With the world population rapidly increasing, transforming our agrifood systems to be more productive, efficient, safe, and sustainable is crucial to mitigate potential food shortages.
no code implementations • 5 Oct 2022 • Tianwen Qian, Ran Cui, Jingjing Chen, Pai Peng, Xiaowei Guo, Yu-Gang Jiang
Considering the fact that the question often remains concentrated in a short temporal range, we propose to first locate the question to a segment in the video and then infer the answer using the located segment only.
1 code implementation • 20 Apr 2022 • Ran Cui, Tianwen Qian, Pai Peng, Elena Daskalaki, Jingjing Chen, Xiaowei Guo, Huyang Sun, Yu-Gang Jiang
Weakly supervised methods only rely on the paired video and query, but the performance is relatively poor.
1 code implementation • ICCV 2021 • Jiawei Zhao, Ke Yan, Yifan Zhao, Xiaowei Guo, Feiyue Huang, Jia Li
Different from these researches, in this paper, we propose a novel Transformer-based Dual Relation learning framework, constructing complementary relationships by exploring two aspects of correlation, i. e., structural relation graph and semantic relation graph.
Ranked #8 on Multi-Label Classification on PASCAL VOC 2007
no code implementations • 2 Aug 2021 • Nenglun Chen, Xingjia Pan, Runnan Chen, Lei Yang, Zhiwen Lin, Yuqiang Ren, Haolei Yuan, Xiaowei Guo, Feiyue Huang, Wenping Wang
We study the problem of weakly supervised grounded image captioning.
1 code implementation • 20 Jul 2021 • ShaoHao Lu, Yuqiao Xian, Ke Yan, Yi Hu, Xing Sun, Xiaowei Guo, Feiyue Huang, Wei-Shi Zheng
The Deep Neural Networks are vulnerable toadversarial exam-ples(Figure 1), making the DNNs-based systems collapsed byadding the inconspicuous perturbations to the images.
1 code implementation • ICCV 2021 • Guanyu Cai, Jun Zhang, Xinyang Jiang, Yifei Gong, Lianghua He, Fufu Yu, Pai Peng, Xiaowei Guo, Feiyue Huang, Xing Sun
However, the performance of existing methods suffers in real life since the user is likely to provide an incomplete description of an image, which often leads to results filled with false positives that fit the incomplete description.
2 code implementations • 8 Jan 2021 • Chenyang Gao, Guanyu Cai, Xinyang Jiang, Feng Zheng, Jun Zhang, Yifei Gong, Pai Peng, Xiaowei Guo, Xing Sun
Secondly, a BERT with locality-constrained attention is proposed to obtain representations of descriptions at different scales.
Ranked #15 on Text based Person Retrieval on CUHK-PEDES
1 code implementation • 10 Dec 2020 • Enwei Zhang, Xinyang Jiang, Hao Cheng, AnCong Wu, Fufu Yu, Ke Li, Xiaowei Guo, Feng Zheng, Wei-Shi Zheng, Xing Sun
Current training objectives of existing person Re-IDentification (ReID) models only ensure that the loss of the model decreases on selected training batch, with no regards to the performance on samples outside the batch.
1 code implementation • NeurIPS 2020 • Fanxu Meng, Hao Cheng, Ke Li, Huixiang Luo, Xiaowei Guo, Guangming Lu, Xing Sun
Through extensive experiments, we demonstrate that SWP is more effective compared to the previous FP-based methods and achieves the state-of-art pruning ratio on CIFAR-10 and ImageNet datasets without obvious accuracy drop.
3 code implementations • 12 Sep 2020 • Jinpeng Wang, Yuting Gao, Ke Li, Jianguo Hu, Xinyang Jiang, Xiaowei Guo, Rongrong Ji, Xing Sun
Specifically, we construct a positive clip and a negative clip for each video.
1 code implementation • 11 Sep 2020 • Fufu Yu, Xinyang Jiang, Yifei Gong, Shizhen Zhao, Xiaowei Guo, Wei-Shi Zheng, Feng Zheng, Xing Sun
Secondly, the Conditional Feature Embedding requires the overall feature of a query image to be dynamically adjusted based on the gallery image it matches, while most of the existing methods ignore the reference images.
Ranked #1 on Person Re-Identification on CUHK03-C
1 code implementation • ECCV 2020 • Shizhen Zhao, Changxin Gao, Jun Zhang, Hao Cheng, Chuchu Han, Xinyang Jiang, Xiaowei Guo, Wei-Shi Zheng, Nong Sang, Xing Sun
In the conventional person Re-ID setting, it is widely assumed that cropped person images are for each individual.
1 code implementation • 27 Jul 2020 • Penghao Zhou, Chong Zhou, Pai Peng, Junlong Du, Xing Sun, Xiaowei Guo, Feiyue Huang
Greedy-NMS inherently raises a dilemma, where a lower NMS threshold will potentially lead to a lower recall rate and a higher threshold introduces more false positives.
Ranked #12 on Object Detection on CrowdHuman (full body)
no code implementations • 16 Jul 2020 • Xiaowei Guo, Teng Liu, Bangbei Tang, Xiaolin Tang, Jinwei Zhang, Wenhao Tan, Shufeng Jin
This paper proposes an adaptive energy management strategy for hybrid electric vehicles by combining deep reinforcement learning (DRL) and transfer learning (TL).
1 code implementation • CVPR 2020 • Xingjia Pan, Yuqiang Ren, Kekai Sheng, Wei-Ming Dong, Haolei Yuan, Xiaowei Guo, Chongyang Ma, Changsheng Xu
However, the detection of oriented and densely packed objects remains challenging because of following inherent reasons: (1) receptive fields of neurons are all axis-aligned and of the same shape, whereas objects are usually of diverse shapes and align along various directions; (2) detection models are typically trained with generic knowledge and may not generalize well to handle specific objects at test time; (3) the limited dataset hinders the development on this task.
1 code implementation • 3 Dec 2019 • Zhihui Zhu, Xinyang Jiang, Feng Zheng, Xiaowei Guo, Feiyue Huang, Wei-Shi Zheng, Xing Sun
Instead of one subspace for each viewpoint, our method projects the feature from different viewpoints into a unified hypersphere and effectively models the feature distribution on both the identity-level and the viewpoint-level.
Ranked #5 on Person Re-Identification on Market-1501 (using extra training data)
1 code implementation • 3 Dec 2019 • Fengxiang Yang, Ke Li, Zhun Zhong, Zhiming Luo, Xing Sun, Hao Cheng, Xiaowei Guo, Feiyue Huang, Rongrong Ji, Shaozi Li
This procedure encourages that the selected training samples can be both clean and miscellaneous, and that the two models can promote each other iteratively.
Ranked #9 on Unsupervised Domain Adaptation on Market to Duke
2 code implementations • 28 Nov 2019 • Xinyang Jiang, Yifei Gong, Xiaowei Guo, Qize Yang, Feiyue Huang, Wei-Shi Zheng, Feng Zheng, Xing Sun
Recently, the research interest of person re-identification (ReID) has gradually turned to video-based methods, which acquire a person representation by aggregating frame features of an entire video.
no code implementations • 6 Aug 2019 • Rongrong Ji, Ke Li, Yan Wang, Xiaoshuai Sun, Feng Guo, Xiaowei Guo, Yongjian Wu, Feiyue Huang, Jiebo Luo
In this paper, we address the problem of monocular depth estimation when only a limited number of training image-depth pairs are available.
1 code implementation • CVPR 2019 • Hong-Xing Yu, Wei-Shi Zheng, An-Cong Wu, Xiaowei Guo, Shaogang Gong, Jian-Huang Lai
To overcome this problem, we propose a deep model for the soft multilabel learning for unsupervised RE-ID.
Ranked #80 on Person Re-Identification on DukeMTMC-reID
1 code implementation • CVPR 2019 • Feng Zheng, Cheng Deng, Xing Sun, Xinyang Jiang, Xiaowei Guo, Zongqiao Yu, Feiyue Huang, Rongrong Ji
Most existing Re-IDentification (Re-ID) methods are highly dependent on precise bounding boxes that enable images to be aligned with each other.
Ranked #2 on Person Re-Identification on CUHK03-C
no code implementations • 5 Dec 2017 • Zhou Yin, Wei-Shi Zheng, An-Cong Wu, Hong-Xing Yu, Hai Wan, Xiaowei Guo, Feiyue Huang, Jian-Huang Lai
While attributes have been widely used for person re-identification (Re-ID) which aims at matching the same person images across disjoint camera views, they are used either as extra features or for performing multi-task learning to assist the image-image matching task.
no code implementations • 12 May 2015 • Baoguang Shi, Cong Yao, Chengquan Zhang, Xiaowei Guo, Feiyue Huang, Xiang Bai
With the rapid increase of transnational communication and cooperation, people frequently encounter multilingual scenarios in various situations.