53 citations · 59 across the 7 of their papers we have counts for
6 papers · 1 filter
Merging Context Clustering with Visual State Space Models for Medical Image Segmentation
Yun Zhu, Dong Zhang, Yi Lin +2
Medical image segmentation demands the aggregation of global and local feature representations, posing a challenge for current methodologies in handling both long-range and short-r…
Efficient Visual State Space Model for Image Deblurring
Lingshun Kong, Jiangxin Dong, Jinhui Tang +2
Convolutional neural networks (CNNs) and Vision Transformers (ViTs) have achieved excellent performance in image restoration. While ViTs generally outperform CNNs by effectively ca…
Context Disentangling and Prototype Inheriting for Robust Visual Grounding
Wei Tang, Liang Li, Xuejing Liu +3
Visual grounding (VG) aims to locate a specific target in an image based on a given language query. The discriminative information from context is important for distinguishing the…
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…
Coupling Global Context and Local Contents for Weakly-Supervised Semantic Segmentation
Chunyan Wang, Dong Zhang, Liyan Zhang +1
Thanks to the advantages of the friendly annotations and the satisfactory performance, Weakly-Supervised Semantic Segmentation (WSSS) approaches have been extensively studied. Rece…