# CaLa: Complementary Association Learning for Augmenting Composed Image Retrieval

arXiv: https://arxiv.org/abs/2405.19149 · HTML: https://mlsanity.com/paper/2405.19149/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 36 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models](https://mlsanity.com/paper/2108.04024/) (2021)
- [All in One: Exploring Unified Video-Language Pre-training](https://mlsanity.com/paper/2203.07303/) (2022)
- [Automatic Spatially-aware Fashion Concept Discovery](https://mlsanity.com/paper/1708.01311/) (2017)
- [Dialog-based Interactive Image Retrieval](https://mlsanity.com/paper/1805.00145/) (2018)
- [ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity](https://mlsanity.com/paper/2203.08101/) (2022)
- [Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark](https://mlsanity.com/paper/2306.02898/) (2023)
- [Sentence-level Prompts Benefit Composed Image Retrieval](https://mlsanity.com/paper/2310.05473/) (2023)
- [Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification](https://mlsanity.com/paper/1803.09937/) (2018)
- [Position Focused Attention Network for Image-Text Matching](https://mlsanity.com/paper/1907.09748/) (2019)
- [Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback](https://mlsanity.com/paper/1905.12794/) (2019)
- [Bi-directional Training for Composed Image Retrieval via Text Prompt Learning](https://mlsanity.com/paper/2303.16604/) (2023)
- [Target-Guided Composed Image Retrieval](https://mlsanity.com/paper/2309.01366/) (2023)
- [Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder](https://mlsanity.com/paper/2305.16304/) (2023)
- [RTIC: Residual Learning for Text and Image Composition using Graph Convolutional Network](https://mlsanity.com/paper/2104.03015/) (2021)
- [KGLM: Integrating Knowledge Graph Structure in Language Models for Link Prediction](https://mlsanity.com/paper/2211.02744/) (2022)
- [Collaborative Group: Composed Image Retrieval via Consensus Learning from Noisy Annotations](https://mlsanity.com/paper/2306.02092/) (2023)
- [Learnable Pillar-based Re-ranking for Image-Text Retrieval](https://mlsanity.com/paper/2304.12570/) (2023)
- [Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning](https://mlsanity.com/paper/2305.12738/) (2023)

## Built on by (most cited first, 21 of 21)

- [OFFSET: Segmentation-based Focus Shift Revision for Composed Image Retrieval](https://mlsanity.com/paper/2507.05631/) (2025)
- [INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval](https://mlsanity.com/paper/2604.18051/) (2026)
- [HABIT: Chrono-Synergia Robust Progressive Learning Framework for Composed Image Retrieval](https://mlsanity.com/paper/2604.18037/) (2026)
- [HINT: Composed Image Retrieval with Dual-path Compositional Contextualized Network](https://mlsanity.com/paper/2603.26341/) (2026)
- [Air-Know: Arbiter-Calibrated Knowledge-Internalizing Robust Network for Composed Image Retrieval](https://mlsanity.com/paper/2604.19386/) (2026)
- [A Comprehensive Survey on Composed Image Retrieval](https://mlsanity.com/paper/2502.18495/) (2025)
- [ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrieval](https://mlsanity.com/paper/2604.20358/) (2026)
- [Beyond Walking: A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search](https://mlsanity.com/paper/2411.17776/) (2024)
- [Scale Efficient Training for Large Datasets](https://mlsanity.com/paper/2503.13385/) (2025)
- [DetailFusion: A Dual-branch Framework with Detail Enhancement for Composed Image Retrieval](https://mlsanity.com/paper/2505.17796/) (2025)
- [Beyond Artificial Misalignment: Detecting and Grounding Semantic-Coordinated Multimodal Manipulations](https://mlsanity.com/paper/2509.12653/) (2025)
- [CalibCLIP: Contextual Calibration of Dominant Semantics for Text-Driven Image Retrieval](https://mlsanity.com/paper/2510.05586/) (2025)
- [ReCALL: Recalibrating Capability Degradation for MLLM-based Composed Image Retrieval](https://mlsanity.com/paper/2602.01639/) (2026)
- [WISER: Wider Search, Deeper Thinking, and Adaptive Fusion for Training-Free Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2602.23029/) (2026)
- [Composed Multi-modal Retrieval: A Survey of Approaches and Applications](https://mlsanity.com/paper/2503.01334/) (2025)
- [From Mapping to Composing: A Two-Stage Framework for Zero-shot Composed Image Retrieval](https://mlsanity.com/paper/2504.17990/) (2025)
- [Automatic Synthesis of High-Quality Triplet Data for Composed Image Retrieval](https://mlsanity.com/paper/2507.05970/) (2025)
- [Heterogeneous Uncertainty-Guided Composed Image Retrieval with Fine-Grained Probabilistic Learning](https://mlsanity.com/paper/2601.11393/) (2026)
- [Efficient Vision Language Model Fine-tuning for Text-based Person Anomaly Search](https://mlsanity.com/paper/2502.03230/) (2025)
- [FACap: A Large-scale Fashion Dataset for Fine-grained Composed Image Retrieval](https://mlsanity.com/paper/2507.07135/) (2025)
- [Beyond Semantic Search: Towards Referential Anchoring in Composed Image Retrieval](https://mlsanity.com/paper/2604.05393/) (2026)

## Abstract (arXiv)

Composed Image Retrieval (CIR) involves searching for target images based on an image-text pair query. While current methods treat this as a query-target matching problem, we argue that CIR triplets contain additional associations beyond this primary relation. In our paper, we identify two new relations within triplets, treating each triplet as a graph node. Firstly, we introduce the concept of text-bridged image alignment, where the query text serves as a bridge between the query image and the target image. We propose a hinge-based cross-attention mechanism to incorporate this relation into network learning. Secondly, we explore complementary text reasoning, considering CIR as a form of cross-modal retrieval where two images compose to reason about complementary text. To integrate these perspectives effectively, we design a twin attention-based compositor. By combining these complementary associations with the explicit query pair-target image relation, we establish a comprehensive set of constraints for CIR. Our framework, CaLa (Complementary Association Learning for Augmenting Composed Image Retrieval), leverages these insights. We evaluate CaLa on CIRR and FashionIQ benchmarks with multiple backbones, demonstrating its superiority in composed image retrieval.