8 citations · 20 across the 39 of their papers we have counts for
14 papers · 2 filters
Towards Ancient Plant Seed Classification: A Benchmark Dataset and Baseline Model
Rui Xing, Runmin Cong, Yingying Wu +5
Understanding the dietary preferences of ancient societies and their evolution across periods and regions is crucial for revealing human-environment interactions. Seeds, as importa…
SAM-DAQ: Segment Anything Model with Depth-guided Adaptive Queries for RGB-D Video Salient Object Detection
Jia Lin, Xiaofei Zhou, Jiyuan Liu +4
Recently segment anything model (SAM) has attracted widespread concerns, and it is often treated as a vision foundation model for universal segmentation. Some researchers have atte…
Empowering DINO Representations for Underwater Instance Segmentation via Aligner and Prompter
Zhiyang Chen, Chen Zhang, Hao Fang +1
Underwater instance segmentation (UIS), integrating pixel-level understanding and instance-level discrimination, is a pivotal technology in marine resource exploration and ecologic…
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…
Semantic Concentration for Self-Supervised Dense Representations Learning
Peisong Wen, Qianqian Xu, Siran Dai +2
Recent advances in image-level self-supervised learning (SSL) have made significant progress, yet learning dense representations for patches remains challenging. Mainstream methods…
UIS-Mamba: Exploring Mamba for Underwater Instance Segmentation via Dynamic Tree Scan and Hidden State Weaken
Runmin Cong, Zongji Yu, Hao Fang +2
Underwater Instance Segmentation (UIS) tasks are crucial for underwater complex scene detection. Mamba, as an emerging state space model with inherently linear complexity and globa…
Stereo-GS: Multi-View Stereo Vision Model for Generalizable 3D Gaussian Splatting Reconstruction
Xiufeng Huang, Ka Chun Cheung, Runmin Cong +2
Generalizable 3D Gaussian Splatting reconstruction showcases advanced Image-to-3D content creation but requires substantial computational resources and large datasets, posing chall…
From Sight to Insight: Unleashing Eye-Tracking in Weakly Supervised Video Salient Object Detection
Qi Qin, Runmin Cong, Gen Zhan +2
The eye-tracking video saliency prediction (VSP) task and video salient object detection (VSOD) task both focus on the most attractive objects in video and show the result in the f…
Proto-FG3D: Prototype-based Interpretable Fine-Grained 3D Shape Classification
Shuxian Ma, Zihao Dong, Runmin Cong +2
Deep learning-based multi-view coarse-grained 3D shape classification has achieved remarkable success over the past decade, leveraging the powerful feature learning capabilities of…
Advancing Marine Research: UWSAM Framework and UIIS10K Dataset for Precise Underwater Instance Segmentation
Hua Li, Shijie Lian, Zhiyuan Li +5
With recent breakthroughs in large-scale modeling, the Segment Anything Model (SAM) has demonstrated significant potential in a variety of visual applications. However, due to the…
PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild
Henghui Ding, Chang Liu, Nikhila Ravi +33
This report provides a comprehensive overview of the 4th Pixel-level Video Understanding in the Wild (PVUW) Challenge, held in conjunction with CVPR 2025. It summarizes the challen…
NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results
Lei Sun, Andrea Alfarano, Peiqi Duan +85
This paper presents an overview of NTIRE 2025 the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results. The primary goal…
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…