SCOT: Self-Supervised Contrastive Pretraining For Zero-Shot Compositional Retrieval

• URL: http://arxiv.org/abs/2501.08347v1
• Date: Sun, 12 Jan 2025 07:23:49 GMT
• Title: SCOT: Self-Supervised Contrastive Pretraining For Zero-Shot Compositional Retrieval
• Authors: Bhavin Jawade, Joao V. B. Soares, Kapil Thadani, Deen Dayal Mohan, Amir Erfan Eshratifar, Benjamin Culpepper, Paloma de Juan, Srirangaraj Setlur, Venu Govindaraju,
• Abstract summary: Compositional image retrieval (CIR) is a multimodal learning task where a model combines a query image with a user-provided text modification to retrieve a target image. Existing methods primarily focus on fully-supervised learning, wherein models are trained on datasets of labeled triplets such as FashionIQ and CIRR. In this work, we propose SCOT, a novel zero-shot compositional pretraining strategy that combines existing large image-text pair datasets with the generative capabilities of large language models to contrastively train an embedding composition network.
• Score: 7.248145893361865
• License:
• Abstract: Compositional image retrieval (CIR) is a multimodal learning task where a model combines a query image with a user-provided text modification to retrieve a target image. CIR finds applications in a variety of domains including product retrieval (e-commerce) and web search. Existing methods primarily focus on fully-supervised learning, wherein models are trained on datasets of labeled triplets such as FashionIQ and CIRR. This poses two significant challenges: (i) curating such triplet datasets is labor intensive; and (ii) models lack generalization to unseen objects and domains. In this work, we propose SCOT (Self-supervised COmpositional Training), a novel zero-shot compositional pretraining strategy that combines existing large image-text pair datasets with the generative capabilities of large language models to contrastively train an embedding composition network. Specifically, we show that the text embedding from a large-scale contrastively-pretrained vision-language model can be utilized as proxy target supervision during compositional pretraining, replacing the target image embedding. In zero-shot settings, this strategy surpasses SOTA zero-shot compositional retrieval methods as well as many fully-supervised methods on standard benchmarks such as FashionIQ and CIRR.

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