CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discrimination

• URL: http://arxiv.org/abs/2408.09441v1
• Date: Sun, 18 Aug 2024 11:23:21 GMT
• Title: CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discrimination
• Authors: Kaicheng Yang, Tiancheng Gu, Xiang An, Haiqiang Jiang, Xiangzi Dai, Ziyong Feng, Weidong Cai, Jiankang Deng,
• Abstract summary: Contrastive Language-Image Pre-training (CLIP) has achieved excellent performance over a wide range of tasks. CLIP heavily relies on a substantial corpus of pre-training data, resulting in notable consumption of computational resources. We introduce CLIP-CID, a novel distillation mechanism that effectively transfers knowledge from a large vision-language foundation model to a smaller model.
• Score: 28.061239778773423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contrastive Language-Image Pre-training (CLIP) has achieved excellent performance over a wide range of tasks. However, the effectiveness of CLIP heavily relies on a substantial corpus of pre-training data, resulting in notable consumption of computational resources. Although knowledge distillation has been widely applied in single modality models, how to efficiently expand knowledge distillation to vision-language foundation models with extensive data remains relatively unexplored. In this paper, we introduce CLIP-CID, a novel distillation mechanism that effectively transfers knowledge from a large vision-language foundation model to a smaller model. We initially propose a simple but efficient image semantic balance method to reduce transfer learning bias and improve distillation efficiency. This method filters out 43.7% of image-text pairs from the LAION400M while maintaining superior performance. After that, we leverage cluster-instance discrimination to facilitate knowledge transfer from the teacher model to the student model, thereby empowering the student model to acquire a holistic semantic comprehension of the pre-training data. Experimental results demonstrate that CLIP-CID achieves state-of-the-art performance on various downstream tasks including linear probe and zero-shot classification.

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