6 citations · 11 across the 12 of their papers we have counts for
12 papers
Expandable, Compressible, Mineable: Open-World Thermal Image Restoration
Pu Li, Huafeng Li, Yafei Zhang +3
In open-world settings, thermal infrared (TIR) image degradations continuously emerge and evolve, while most existing all-in-one restoration methods are built on a closed-set assum…
Hierarchical Prompt Learning for Image- and Text-Based Person Re-Identification
Linhan Zhou, Shuang Li, Neng Dong +3
Person re-identification (ReID) aims to retrieve target pedestrian images given either visual queries (image-to-image, I2I) or textual descriptions (text-to-image, T2I). Although b…
DINOv2 Driven Gait Representation Learning for Video-Based Visible-Infrared Person Re-identification
Yujie Yang, Shuang Li, Jun Ye +3
Video-based Visible-Infrared person re-identification (VVI-ReID) aims to retrieve the same pedestrian across visible and infrared modalities from video sequences. Existing methods…
Diverse Semantics-Guided Feature Alignment and Decoupling for Visible-Infrared Person Re-Identification
Neng Dong, Shuanglin Yan, Liyan Zhang +1
Visible-Infrared Person Re-Identification (VI-ReID) is a challenging task due to the large modality discrepancy between visible and infrared images, which complicates the alignment…
ShapeSpeak: Body Shape-Aware Textual Alignment for Visible-Infrared Person Re-Identification
Shuanglin Yan, Neng Dong, Shuang Li +3
Visible-Infrared Person Re-identification (VIReID) aims to match visible and infrared pedestrian images, but the modality differences and the complexity of identity features make i…
Embedding and Enriching Explicit Semantics for Visible-Infrared Person Re-Identification
Neng Dong, Shuanglin Yan, Liyan Zhang +1
Visible-infrared person re-identification (VIReID) retrieves pedestrian images with the same identity across different modalities. Existing methods learn visual content solely from…
Prototypical Prompting for Text-to-image Person Re-identification
Shuanglin Yan, Jun Liu, Neng Dong +2
In this paper, we study the problem of Text-to-Image Person Re-identification (TIReID), which aims to find images of the same identity described by a text sentence from a pool of c…
CLIP-Driven Semantic Discovery Network for Visible-Infrared Person Re-Identification
Xiaoyan Yu, Neng Dong, Liehuang Zhu +2
Visible-infrared person re-identification (VIReID) primarily deals with matching identities across person images from different modalities. Due to the modality gap between visible…
Multi-view Information Integration and Propagation for Occluded Person Re-identification
Neng Dong, Shuanglin Yan, Hao Tang +2
Occluded person re-identification (re-ID) presents a challenging task due to occlusion perturbations. Although great efforts have been made to prevent the model from being disturbe…
Learning Comprehensive Representations with Richer Self for Text-to-Image Person Re-Identification
Shuanglin Yan, Neng Dong, Jun Liu +2
Text-to-image person re-identification (TIReID) retrieves pedestrian images of the same identity based on a query text. However, existing methods for TIReID typically treat it as a…
Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification
Neng Dong, Liyan Zhang, Shuanglin Yan +2
Occlusion perturbation presents a significant challenge in person re-identification (re-ID), and existing methods that rely on external visual cues require additional computational…
CLIP-Driven Fine-grained Text-Image Person Re-identification
Shuanglin Yan, Neng Dong, Liyan Zhang +1
TIReID aims to retrieve the image corresponding to the given text query from a pool of candidate images. Existing methods employ prior knowledge from single-modality pre-training t…