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From the 1 of 11 linked papers with an AI index.

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11 papers

cs.CV2026

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…

cs.CV2026

Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making

Ruoyu Chen, Shangquan Sun, Xiaoqing Guo +8

The paper introduces a training approach that encodes human‑provided region priors and uses a subset‑selection attribution method to penalize models when their decision evidence fa…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.LG2026

Can Attribution Predict Risk? From Multi-View Attribution to Planning Risk Signals in End-to-End Autonomous Driving

Le Yang, Ruoyu Chen, Haijun Liu +3

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.…