Publications (7)
Believe Your Model: Distribution-Guided Confidence Calibration
Xizhong Yang, Haotian Zhang, Huiming Wang +1
Large Reasoning Models have demonstrated remarkable performance with the advancement of test-time scaling techniques, which enhances prediction accuracy by generating multiple cand…
Towards Stable Co-saliency Detection and Object Co-segmentation
Bo Li, Lv Tang, Senyun Kuang +2
In this paper, we present a novel model for simultaneous stable co-saliency detection (CoSOD) and object co-segmentation (CoSEG). To detect co-saliency (segmentation) accurately, t…
Semantic Bridging Domains: Pseudo-Source as Test-Time Connector
Xizhong Yang, Huiming Wang, Ning Xu +1
Distribution shifts between training and testing data are a critical bottleneck limiting the practical utility of models, especially in real-world test-time scenarios. To adapt mod…
MM-Point: Multi-View Information-Enhanced Multi-Modal Self-Supervised 3D Point Cloud Understanding
Hai-Tao Yu, Mofei Song
In perception, multiple sensory information is integrated to map visual information from 2D views onto 3D objects, which is beneficial for understanding in 3D environments. But in…
From the Inside Out: Progressive Distribution Refinement for Confidence Calibration
Xizhong Yang, Yinan Xia, Huiming Wang +1
Leveraging the model's internal information as the self-reward signal in Reinforcement Learning (RL) has received extensive attention due to its label-free nature. While prior work…
Similarity and Dissimilarity Guided Co-association Matrix Construction for Ensemble Clustering
Xu Zhang, Yuheng Jia, Mofei Song +1
Ensemble clustering aggregates multiple weak clusterings to achieve a more accurate and robust consensus result. The Co-Association matrix (CA matrix) based method is the mainstrea…
Disentangled High Quality Salient Object Detection
Lv Tang, Bo Li, Shouhong Ding +1
Aiming at discovering and locating most distinctive objects from visual scenes, salient object detection (SOD) plays an essential role in various computer vision systems. Coming to…