2 citations · 2 across the 10 of their papers we have counts for
8 papers · 1 filter
Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations
Xianghao Jiao, Ruoyu Chen, Wei Wang +6
Attribution methods are widely used to characterize the evidence underlying model predictions, yet their potential to improve model behavior remains underexplored. Attribution inco…
Domain Adaptive Object Detection via Dual-Stream Bilevel-Cycle Optimization
Yannan Chen, Wei Wang, Wenqiang Wang +5
Cycle self-training (CST) breaks the shared classifier assumption of the standard self-training framework, which is effective for unsupervised domain adaptation and exploits unlabe…
PhaseWin: An Efficient Search Algorithm for Faithful Visual Attribution
Zihan Gu, Junchi Zhang, Li Liu +2
Visual attribution is a fundamental tool for interpreting modern vision and vision-language models, particularly when their decisions must be inspected, diagnosed, or audited. Its…
Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints
Ruoyu Chen, Shangquan Sun, Xiaoqing Guo +8
Reliable models should not only predict correctly, but also base their decisions on acceptable evidence. However, conventional supervised learning typically provides only class-lev…
PhaseWin Search Framework Enable Efficient Object-Level Interpretation
Zihan Gu, Ruoyu Chen, Junchi Zhang +3
Attribution is essential for interpreting object-level foundation models. Recent methods based on submodular subset selection have achieved high faithfulness, but their efficiency…
Did Models Learn Sufficiently? Attribution-Guided Training via Subset-Selected Counterfactual Augmentation
Yannan Chen, Ruoyu Chen, Wei Wang +6
Current visual models often make predictions based on a limited set of discriminative visual cues. As a result, they may become unreliable when the distribution shifts or when thes…
FaceInsight: A Multimodal Large Language Model for Face Perception
Jingzhi Li, Changjiang Luo, Ruoyu Chen +4
Recent advances in multimodal large language models (MLLMs) have demonstrated strong capabilities in understanding general visual content. However, these general-domain MLLMs perfo…
Interpreting Object-level Foundation Models via Visual Precision Search
Ruoyu Chen, Siyuan Liang, Jingzhi Li +5
Advances in multimodal pre-training have propelled object-level foundation models, such as Grounding DINO and Florence-2, in tasks like visual grounding and object detection. Howev…