9 citations · 13 across the 15 of their papers we have counts for
16 papers
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…
Can Attribution Predict Risk? From Multi-View Attribution to Planning Risk Signals in End-to-End Autonomous Driving
Le Yang, Haijun Liu, Jiawei Liang +2
End-to-end autonomous driving models generate future trajectories from multi-view inputs, improving system integration but introducing opaque decisions and hard-to-localize risks.…
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…
Where MLLMs Attend and What They Rely On: Explaining Autoregressive Token Generation
Ruoyu Chen, Xiaoqing Guo, Kangwei Liu +6
Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in aligning visual inputs with natural language outputs. Yet, the extent to which generated token…
Phantom-Insight: Adaptive Multi-cue Fusion for Video Camouflaged Object Detection with Multimodal LLM
Hua Zhang, Changjiang Luo
Video camouflaged object detection (VCOD) is challenging due to dynamic environments. Existing methods face two main issues: (1) SAM-based methods struggle to separate camouflaged…
SMA: Who Said That? Auditing Membership Leakage in Semi-Black-box RAG Controlling
Shixuan Sun, Siyuan Liang, Jianjie Huang +2
Retrieval-Augmented Generation (RAG) and its Multimodal Retrieval-Augmented Generation (MRAG) significantly improve the knowledge coverage and contextual understanding of Large Lan…
Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding
Youze Wang, Zijun Chen, Ruoyu Chen +8
Recent advancements in multimodal large language models for video understanding (videoLLMs) have enhanced their capacity to process complex spatiotemporal data. However, challenges…
Deconstructing Positional Information: From Attention Logits to Training Biases
Zihan Gu, Ruoyu Chen, Han Zhang +2
Positional encodings enable Transformers to incorporate sequential information, yet their theoretical understanding remains limited to two properties: distance attenuation and tran…
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…
Beyond Progress Measures: Theoretical Insights into the Mechanism of Grokking
Zihan Gu, Ruoyu Chen, Hua Zhang +2
Grokking, referring to the abrupt improvement in test accuracy after extended overfitting, offers valuable insights into the mechanisms of model generalization. Existing researches…
Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object Detection
Ruoyu Chen, Hua Zhang, Jingzhi Li +3
The objective of few-shot object detection (FSOD) is to detect novel objects with few training samples. The core challenge of this task is how to construct a generalized feature sp…
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…
Object Detectors in the Open Environment: Challenges, Solutions, and Outlook
Siyuan Liang, Wei Wang, Ruoyu Chen +5
With the emergence of foundation models, deep learning-based object detectors have shown practical usability in closed set scenarios. However, for real-world tasks, object detector…