1 code implementation • 7 Nov 2023 • Neng Dong, Shuanglin Yan, Hao Tang, Jinhui Tang, Liyan Zhang
Moreover, as multiple images with the same identity are not accessible in the testing stage, we devise an Information Propagation (IP) mechanism to distill knowledge from the comprehensive representation to that of a single occluded image.
no code implementations • 17 Oct 2023 • Shuanglin Yan, Neng Dong, Jun Liu, Liyan Zhang, Jinhui Tang
Since the support set is unavailable during inference, we propose to distill the knowledge learned by the "richer" model into a lightweight model for inference with a single image/text as input.
no code implementations • 29 Sep 2023 • Tiantian Gong, Guodong Du, Junsheng Wang, Yongkang Ding, Liyan Zhang
Therefore, we propose the cross-modal nearest neighbor construction strategy for missing data by computing the cross-modal similarity between existing images and texts, which provides key guidance for the completion of missing modal features.
1 code implementation • 14 Jul 2023 • Neng Dong, Liyan Zhang, Shuanglin Yan, Hao Tang, Jinhui Tang
Occlusion perturbation presents a significant challenge in person re-identification (re-ID), and existing methods that rely on external visual cues require additional computational resources and only consider the issue of missing information caused by occlusion.
1 code implementation • 18 Apr 2023 • Chunyan Wang, Dong Zhang, Liyan Zhang, Jinhui Tang
Specifically, a flexible context aggregation module is proposed to capture the global object context in different granular spaces.
Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation
1 code implementation • 23 Jan 2023 • Fei Shen, Xiaoyu Du, Liyan Zhang, Xiangbo Shu, Jinhui Tang
To address this problem, in this paper, we propose a simple Triplet Contrastive Representation Learning (TCRL) framework which leverages cluster features to bridge the part features and global features for unsupervised vehicle re-identification.
1 code implementation • 19 Oct 2022 • Shuanglin Yan, Neng Dong, Liyan Zhang, Jinhui Tang
Secondly, cross-grained feature refinement (CFR) and fine-grained correspondence discovery (FCD) modules are proposed to establish the cross-grained and fine-grained interactions between modalities, which can filter out non-modality-shared image patches/words and mine cross-modal correspondences from coarse to fine.
1 code implementation • 5 Oct 2022 • Yu Quan, Dong Zhang, Liyan Zhang, Jinhui Tang
To address this problem, in this paper, we propose a Centralized Feature Pyramid (CFP) for object detection, which is based on a globally explicit centralized feature regulation.
no code implementations • 30 Aug 2022 • Shuanglin Yan, Hao Tang, Liyan Zhang, Jinhui Tang
Moreover, existing methods seldom consider the information inequality problem between modalities caused by image-specific information.
1 code implementation • 13 Dec 2021 • Dong Liang, Ling Li, Mingqiang Wei, Shuo Yang, Liyan Zhang, Wenhan Yang, Yun Du, Huiyu Zhou
Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images.
1 code implementation • 21 Mar 2021 • Zongqi Wei, Dong Liang, Dong Zhang, Liyan Zhang, Qixiang Geng, Mingqiang Wei, Huiyu Zhou
Specifically, for a given set of feature maps, CG first computes the feature similarity between each channel and the remaining channels as the intermediary calibration guidance.
no code implementations • CVPR 2021 • Dongyan Guo, Yanyan Shao, Ying Cui, Zhenhua Wang, Liyan Zhang, Chunhua Shen
We propose to establish part-to-part correspondence between the target and the search region with a complete bipartite graph, and apply the graph attention mechanism to propagate target information from the template feature to the search feature.
no code implementations • 29 Sep 2019 • Xiangbo Shu, Liyan Zhang, Guo-Jun Qi, Wei Liu, Jinhui Tang
To this end, we propose a novel Skeleton-joint Co-attention Recurrent Neural Networks (SC-RNN) to capture the spatial coherence among joints, and the temporal evolution among skeletons simultaneously on a skeleton-joint co-attention feature map in spatiotemporal space.
no code implementations • 4 Jun 2017 • Xiangbo Shu, Jinhui Tang, Zechao Li, Hanjiang Lai, Liyan Zhang, Shuicheng Yan
Basically, for each age group, we learn an aging dictionary to reveal its aging characteristics (e. g., wrinkles), where the dictionary bases corresponding to the same index yet from two neighboring aging dictionaries form a particular aging pattern cross these two age groups, and a linear combination of all these patterns expresses a particular personalized aging process.
no code implementations • 3 Jun 2017 • Xiangbo Shu, Jinhui Tang, Guo-Jun Qi, Yan Song, Zechao Li, Liyan Zhang
To this end, we propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to model the long-term inter-related dynamics between two interacting people on the bounding boxes covering people.
Ranked #2 on Human Interaction Recognition on BIT