Research map / Vision-language and multimodal / CLIP and zero-shot
CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discrimination
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Proposes CLIP-CID, a distillation framework for compressing CLIP into smaller models using cluster-instance discrimination, though the supplied text is only the AAAI template and the method details are not available.
Problem
Distilling CLIP-style vision-language models efficiently, since CLIP's effectiveness relies on large pre-training corpora and heavy compute; the abstract says knowledge distillation has been applied to single-modality models but not easily extended to vision-language foundation models.
Approach
Uses cluster-instance discrimination for CLIP knowledge distillation; the specific mechanism could not be verified because the supplied full text is the AAAI author template rather than the paper body.
Limitations
- Supplied text is only the AAAI formatting template; the method, experiments and results are not present.
Experimental setup
Builds on: CLIP knowledge distillation
Closest papers by meaning
- CLIP-KD: An Empirical Study of CLIP Model Distillation (CVPR 2024 · similarity 0.92)
- Building Vision-Language Models on Solid Foundations with Masked Distillation (CVPR 2024 · similarity 0.91)
- Post-pre-training for Modality Alignment in Vision-Language Foundation Models (CVPR 2025 · similarity 0.90)
- VITRIX-CLIPIN: Enhancing Fine-Grained Visual Understanding in CLIP via Instruction-Editing Data and Long Captions (NeurIPS 2025 · similarity 0.89)
- Diffusion Feedback Helps CLIP See Better (ICLR 2025 · similarity 0.88)
- Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual Knowledge (NeurIPS 2024 · similarity 0.88)
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