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Factorized MLLM Embeddings for Composed Image Retrieval

Paper recorded by Signals 4 on 2026-11-03 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-03 on arXiv · recorded by Signals 4 on 2026-11-04

Category: cs.CV · 计算机视觉 · first seen 2026-11-04

Abstract

Composed Image Retrieval (CIR) aims to retrieve a target image given a reference image and a modification text. Although recent approaches leveraging Multimodal Large Language Models (MLLMs) have shown significant progress, we argue that the MLLM's compositional understanding remains underutilized. This stems from directly mapping composed queries into a retrieval embedding via contrastive supervi

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#15 most recent of 710 cs.CV papers we have recorded · ↑ newer: Robust Reconstruction of Power Lines from Incomplete Mobile and Aerial · ↓ older: From Natural Images to Medical Scans: Transferability of Diffusion Mod
Cite this page: Factorized MLLM Embeddings for Composed Image Retrieval: the #15 most recent of 710 cs.CV papers we have recorded (as of 2026-11-03). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/factorized-mllm-embeddings-for-composed-image-retrieval.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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