ArXiv

CoCo-IR: Contextual Composed Image Retrieval

2026-08-05 · 17:59 UTC ·Shengcao Cao, Tanmaya Shekhar Dabral, Zhongli Ding... ·1 min read
Authors
Shengcao Cao, Tanmaya Shekhar Dabral, Zhongli Ding...
Categories
cs.CV
arXiv
https://arxiv.org/abs/2608.05149v1
PDF
https://arxiv.org/pdf/2608.05149v1

Brief

CoCo-IR proposes a multi-turn contextual composed image retrieval task and an LMM-based context-aware reasoner that produces Transformable Image Embeddings (TIE) which evolve across interaction turns, supported by an autonomous LMM-driven data engine with model-guided hard-negative mining. Based on the abstract (full text not provided), it reports 39.4 mAP@5 on CIRCO and 44.1 R@1 on 4-turn CoCo-IR (vs 28.2 R@1 prior).

Why it matters

Introduces CoCo-IR: a multi-turn contextual composed image retrieval task and an LMM-based context-aware reasoner that generates Transformable Image Embeddings (TIE) evolving across turns; training data is produced by an autonomous LMM-driven data engine with model-guided verification to mine hard negatives.

Key details

  • Strong empirical gains: 39.4 mAP@5 on the single-turn CIRCO benchmark; on the new CoCo-IR 4-turn benchmark the model achieves 44.1 R@1 versus 28.2 R@1 from existing methods, demonstrating large improvements for multi-turn retrieval.
  • Paper by Shengcao Cao, Tanmaya Shekhar Dabral, Zhongli Ding, et al., posted to arXiv 2026-08-05 and listed for ECCV 2026; project page: https://CoCo-IR.github.io.
Source evidence

Abstract

Current instruction-based image retrieval systems are powerful but limited to single-turn interactions, failing to capture the iterative nature of complex, real-world visual searches. To overcome this limitation, we introduce Contextual Composed Image Retrieval (CoCo-IR), a novel task that enables users to progressively refine search results through interactions. We address this new task by proposing a new model based on a Large Multimodal Model (LMM) that functions as a context-aware reasoner for CoCo-IR. Our model interprets the entire interaction history to generate Transformable Image Embeddings (TIE) that evolve across turns. To fuel the model training without expensive human annotations, we develop a fully autonomous, scalable data engine that leverages LMMs to generate high-quality contextual retrieval data, and uses model-guided verification to mine challenging hard negatives. Extensive experiments demonstrate that our approach establishes new state-of-the-art performance: We achieve 39.4 mAP@5 on the challenging single-turn benchmark CIRCO; furthermore, on our new CoCo-IR benchmark, our model maintains robust performance with 44.1 R@1 on 4-turn dialogues, dramatically outperforming existing methods (28.2 4-turn R@1) that fail to handle multi-turn context. Project page: https://CoCo-IR.github.io.

Comment: ECCV 2026