SCOT: Self-supervised Contrastive Pretraining For Zero-shot Compositional Retrieval

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 ut

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyjawade2025scot

Related papers

• From Mapping To Composing: A Two-stage Framework For Zero-shot Composed Image Retrieval (2025)0.00
• Zero-shot Composed Text-image Retrieval (2023)0.00
• Pretrain Like Your Inference: Masked Tuning Improves Zero-shot Composed Image Retrieval (2023)2.86
• Training-free Zero-shot Composed Image Retrieval With Local Concept Reranking (2023)0.00
• Compositional Image Retrieval Via Instruction-aware Contrastive Learning (2024)0.00
• Context-cir: Learning From Concepts In Text For Composed Image Retrieval (2025)4.67
• Cotmr: Chain-of-thought Multi-scale Reasoning For Training-free Zero-shot Composed Image Retrieval (2025)0.00
• Mcot-re: Multi-faceted Chain-of-thought And Re-ranking For Training-free Zero-shot Composed Image Retrieval (2025)0.00