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cs.CV2026

GRAPE: Let GRPO Supervise Query Rewriting by Ranking for Retrieval

Zhaohua Zhang, Jianhuan Zhuo, Muxi Chen +8

The CLIP model has established itself as a cornerstone of large-scale retrieval systems. However, its performance often degrades under distributional shifts such as multilingual, l…

cs.CV2026

FBCIR: Balancing Cross-Modal Focuses in Composed Image Retrieval

Chenchen Zhao, Jianhuan Zhuo, Muxi Chen +6

Composed image retrieval (CIR) requires multi-modal models to jointly reason over visual content and semantic modifications presented in text-image input pairs. While current CIR m…

cs.CV2026

\textit{FocaLogic}: Logic-Based Interpretation of Visual Model Decisions

Chenchen Zhao, Muxi Chen, Qiang Xu

Interpretability of modern visual models is crucial, particularly in high-stakes applications. However, existing interpretability methods typically suffer from either reliance on w…

cs.CV2025

FailureAtlas:Mapping the Failure Landscape of T2I Models via Active Exploration

Muxi Chen, Zhaohua Zhang, Chenchen Zhao +8

Static benchmarks have provided a valuable foundation for comparing Text-to-Image (T2I) models. However, their passive design offers limited diagnostic power, struggling to uncover…

cs.CV2025

HiBug2: Efficient and Interpretable Error Slice Discovery for Comprehensive Model Debugging

Muxi Chen, Chenchen Zhao, Qiang Xu

Despite the significant success of deep learning models in computer vision, they often exhibit systematic failures on specific data subsets, known as error slices. Identifying and…