129 citations · 205 across the 12 of their papers we have counts for
13 papers
Time-Unconditional Generative Speech Enhancement via Autonomous Rectified Flow
Wen Zhang, Wenbin Jiang, Yang Zhang +1
Most generative speech enhancement methods rely on explicit time-step embeddings for temporal conditioning. In this paper, we propose the Autonomous Rectified Flow framework, which…
M-SAM: Multi-Modal Mixture-of-Experts with Memory-Augmented SAM for RGB-D Video Salient Object Detection
Jiyuan Liu, Jia Lin, Xiaofei Zhou +3
The Segment Anything Model 2 (SAM2) has emerged as a foundation model for universal segmentation. Owing to its generalizable visual representations, SAM2 has been successfully appl…
G2HFNet: GeoGran-Aware Hierarchical Feature Fusion Network for Salient Object Detection in Optical Remote Sensing Images
Bin Wan, Runmin Cong, Xiaofei Zhou +3
Remote sensing images captured from aerial perspectives often exhibit significant scale variations and complex backgrounds, posing challenges for salient object detection (SOD). Ex…
RSONet: Region-guided Selective Optimization Network for RGB-T Salient Object Detection
Bin Wan, Runmin Cong, Xiaofei Zhou +3
This paper focuses on the inconsistency in salient regions between RGB and thermal images. To address this issue, we propose the Region-guided Selective Optimization Network for RG…
RDNet: Region Proportion-Aware Dynamic Adaptive Salient Object Detection Network in Optical Remote Sensing Images
Bin Wan, Runmin Cong, Xiaofei Zhou +3
Salient object detection (SOD) in remote sensing images faces significant challenges due to large variations in object sizes, the computational cost of self-attention mechanisms, a…
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…
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…
Expertise-aware Multi-LLM Recruitment and Collaboration for Medical Decision-Making
Liuxin Bao, Zhihao Peng, Xiaofei Zhou +3
Medical Decision-Making (MDM) is a complex process requiring substantial domain-specific expertise to effectively synthesize heterogeneous and complicated clinical information. Whi…
WXSOD: A Benchmark for Robust Salient Object Detection in Adverse Weather Conditions
Quan Chen, Xiong Yang, Bolun Zheng +5
Salient object detection (SOD) in complex environments remains a challenging research topic. Most existing methods perform well in natural scenes with negligible noise, and tend to…
AIM 2024 Challenge on Video Saliency Prediction: Methods and Results
Andrey Moskalenko, Alexey Bryncev, Dmitry Vatolin +30
This paper reviews the Challenge on Video Saliency Prediction at AIM 2024. The goal of the participants was to develop a method for predicting accurate saliency maps for the provid…
MINet: Multi-scale Interactive Network for Real-time Salient Object Detection of Strip Steel Surface Defects
Kunye Shen, Xiaofei Zhou, Zhi Liu
The automated surface defect detection is a fundamental task in industrial production, and the existing saliencybased works overcome the challenging scenes and give promising detec…
Quality-aware Selective Fusion Network for V-D-T Salient Object Detection
Liuxin Bao, Xiaofei Zhou, Xiankai Lu +5
Depth images and thermal images contain the spatial geometry information and surface temperature information, which can act as complementary information for the RGB modality. Howev…
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…