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Zhimeng Huang

2 accepted papers

2026

Decoupling Template Bias in CLIP: Harnessing Empty Prompts for Enhanced Few-Shot Learning

AAAI 2026technical

The Contrastive Language-Image Pre-Training (CLIP) model excels in few-shot learning by aligning visual and textual representations. Our study shows that template-sample similarity (TSS), defined as the resemblance between a text template and an image sample, introduces bias. This bias leads the mod

Cited by 0SourcePDFScholar
2026

Discovering Adaptive Task Dependencies for Efficient Multi-Task Representation Compression

CVPR 2026

Traditional image compression prioritizes pixel fidelity but often preserves details irrelevant to downstream vision tasks. Compressing task-specific representations instead better aligns with task semantics, yet redundant information persists across correlated tasks. Existing multi-task compression

Cited by 0SourceScholar