Skip to yearly menu bar Skip to main content


Poster

Visual Delta Generator with Large Multi-modal Models for Semi-supervised Composed Image Retrieval

Young Kyun Jang · Donghyun Kim · Zihang Meng · Dat Huynh · Ser-Nam Lim

Arch 4A-E Poster #214
[ ] [ Project Page ] [ Paper PDF ]
[ Poster
Thu 20 Jun 5 p.m. PDT — 6:30 p.m. PDT

Abstract:

Composed Image Retrieval (CIR) is a task that retrieves images similar to a query, based on a provided textual modification. Current techniques rely on supervised learning for CIR models using labeled triplets of the . These specific triplets are not as commonly available as simple image-text pairs, limiting the widespread use of CIR and its scalability. On the other hand, zero-shot CIR can be relatively easily trained with image-caption pairs without considering the image-to-image relation, but this approach tends to yield lower accuracy. We propose a new semi-supervised CIR approach where we search for a reference and its related target images in auxiliary data and learn our large language model-based Visual Delta Generator (VDG) to generate text describing the visual difference (i.e., visual delta) between the two. VDG, equipped with fluent language knowledge and being model agnostic, can generate pseudo triplets to boost the performance of CIR models. Our approach significantly improves the existing supervised learning approaches and achieves state-of-the-art results on the CIR benchmarks.

Chat is not available.