1 citations · 1 across the 7 of their papers we have counts for
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Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception
Mingfeng Zha, Tianyu Li, Guoqing Wang +5
Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes. Existing methods, constrained…
UAV as Urban Construction Change Monitor: A New Benchmark and Change Captioning Model
Yupeng Gao, Tianyu Li, Guoqing Wang +1
Remote Sensing Image Change Captioning (RSICC) aims to generate spatially grounded natural language descriptions of scene evolution from bi-temporal imagery, moving beyond binary c…
Topology-Aware Layer Pruning for Large Vision-Language Models
Pengcheng Zheng, Chaoning Zhang, Ya Wen +10
Large Language Models (LLMs) have demonstrated strong capabilities in natural language understanding and reasoning, while recent extensions that incorporate visual inputs enable th…
Implicit Counterfactual Learning for Audio-Visual Segmentation
Mingfeng Zha, Tianyu Li, Guoqing Wang +4
Audio-visual segmentation (AVS) aims to segment objects in videos based on audio cues. Existing AVS methods are primarily designed to enhance interaction efficiency but pay limited…
DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing
Minghang Zhou, Tianyu Li, Chaofan Qiao +5
Multispectral oriented object detection faces challenges due to both inter-modal and intra-modal discrepancies. Recent studies often rely on transformer-based models to address the…
Region-aware Distribution Contrast: A Novel Approach to Multi-Task Partially Supervised Learning
Meixuan Li, Tianyu Li, Guoqing Wang +3
In this study, we address the intricate challenge of multi-task dense prediction, encompassing tasks such as semantic segmentation, depth estimation, and surface normal estimation,…
JoReS-Diff: Joint Retinex and Semantic Priors in Diffusion Model for Low-light Image Enhancement
Yuhui Wu, Guoqing Wang, Zhiwen Wang +5
Low-light image enhancement (LLIE) has achieved promising performance by employing conditional diffusion models. Despite the success of some conditional methods, previous methods m…