11 citations · 19 across the 10 of their papers we have counts for
10 papers
Divide-and-Conquer Decoupled Network for Cross-Domain Few-Shot Segmentation
Runmin Cong, Anpeng Wang, Bin Wan +3
Cross-domain few-shot segmentation (CD-FSS) aims to tackle the dual challenge of recognizing novel classes and adapting to unseen domains with limited annotations. However, encoder…
GA2-CLIP: Generic Attribute Anchor for Efficient Prompt Tuningin Video-Language Models
Bin Wang, Ruotong Hu, Wentong Li +5
Visual and textual soft prompt tuning can effectively improve the adaptability of Vision-Language Models (VLMs) in downstream tasks. However, fine-tuning on video tasks impairs the…
The 1st Solution for 4th PVUW MeViS Challenge: Unleashing the Potential of Large Multimodal Models for Referring Video Segmentation
Hao Fang, Runmin Cong, Xiankai Lu +2
Motion expression video segmentation is designed to segment objects in accordance with the input motion expressions. In contrast to the conventional Referring Video Object Segmenta…
Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images
Fuxiang Feng, Runmin Cong, Shoushui Wei +4
Reconstructing Hyperspectral Images (HSI) from RGB images can yield high spatial resolution HSI at a lower cost, demonstrating significant application potential. This paper reveals…
UNINEXT-Cutie: The 1st Solution for LSVOS Challenge RVOS Track
Hao Fang, Feiyu Pan, Xiankai Lu +2
Referring video object segmentation (RVOS) relies on natural language expressions to segment target objects in video. In this year, LSVOS Challenge RVOS Track replaced the origin Y…
Video Object Segmentation via SAM 2: The 4th Solution for LSVOS Challenge VOS Track
Feiyu Pan, Hao Fang, Runmin Cong +2
Video Object Segmentation (VOS) task aims to segmenting a particular object instance throughout the entire video sequence given only the object mask of the first frame. Recently, S…
Query-guided Prototype Evolution Network for Few-Shot Segmentation
Runmin Cong, Hang Xiong, Jinpeng Chen +3
Previous Few-Shot Segmentation (FSS) approaches exclusively utilize support features for prototype generation, neglecting the specific requirements of the query. To address this, w…
SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection
Runmin Cong, Yuchen Guan, Jinpeng Chen +3
Despite significant progress in shadow detection, current methods still struggle with the adverse impact of background color, which may lead to errors when shadows are present on c…
Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection
Runmin Cong, Hongyu Liu, Chen Zhang +4
By integrating complementary information from RGB image and depth map, the ability of salient object detection (SOD) for complex and challenging scenes can be improved. In recent y…
Frequency Perception Network for Camouflaged Object Detection
Runmin Cong, Mengyao Sun, Sanyi Zhang +3
Camouflaged object detection (COD) aims to accurately detect objects hidden in the surrounding environment. However, the existing COD methods mainly locate camouflaged objects in t…