ComCLIP: Training-Free Compositional Image and Text Matching

• URL: http://arxiv.org/abs/2211.13854v5
• Date: Sat, 13 Apr 2024 00:14:03 GMT
• Title: ComCLIP: Training-Free Compositional Image and Text Matching
• Authors: Kenan Jiang, Xuehai He, Ruize Xu, Xin Eric Wang,
• Abstract summary: Contrastive Language-Image Pretraining has demonstrated great zero-shot performance for matching images and text. We propose a novel textbftextittraining-free compositional CLIP model (ComCLIP) ComCLIP disentangles input images into subjects, objects, and action sub-images and composes CLIP's vision encoder and text encoder to perform evolving matching over compositional text embedding and sub-image embeddings.
• Score: 19.373706257771673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contrastive Language-Image Pretraining (CLIP) has demonstrated great zero-shot performance for matching images and text. However, it is still challenging to adapt vision-lanaguage pretrained models like CLIP to compositional image and text matching -- a more challenging image and text matching task requiring the model understanding of compositional word concepts and visual components. Towards better compositional generalization in zero-shot image and text matching, in this paper, we study the problem from a causal perspective: the erroneous semantics of individual entities are essentially confounders that cause the matching failure. Therefore, we propose a novel \textbf{\textit{training-free}} compositional CLIP model (ComCLIP). ComCLIP disentangles input images into subjects, objects, and action sub-images and composes CLIP's vision encoder and text encoder to perform evolving matching over compositional text embedding and sub-image embeddings. In this way, ComCLIP can mitigate spurious correlations introduced by the pretrained CLIP models and dynamically evaluate the importance of each component. Experiments on four compositional image-text matching datasets: SVO, ComVG, Winoground, and VL-checklist, and two general image-text retrieval datasets: Flick30K, and MSCOCO demonstrate the effectiveness of our plug-and-play method, which boosts the \textbf{\textit{zero-shot}} inference ability of CLIP, SLIP, and BLIP2 even without further training or fine-tuning. Our codes can be found at https://github.com/eric-ai-lab/ComCLIP.

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