LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation

TL;DR

This paper reviews recent advances in integrating large multimodal models with object-centric vision, focusing on understanding, segmentation, editing, and generation of visual objects.

Contribution

It provides a structured overview of the key paradigms, strategies, and challenges in developing object-centric multimodal vision systems.

Findings

Organized literature into four major themes: understanding, segmentation, editing, and generation.

Summarized modeling paradigms, learning strategies, and evaluation protocols.

Discussed open challenges like instance permanence and spatial control.

Abstract

Large Multimodal Models (LMMs) have achieved remarkable progress in general-purpose vision--language understanding, yet they remain limited in tasks requiring precise object-level grounding, fine-grained spatial reasoning, and controllable visual manipulation. In particular, existing systems often struggle to identify the correct instance, preserve object identity across interactions, and localize or modify designated regions with high precision. Object-centric vision provides a principled framework for addressing these challenges by promoting explicit representations and operations over visual entities, thereby extending multimodal systems from global scene understanding to object-level understanding, segmentation, editing, and generation. This paper presents a comprehensive review of recent advances at the convergence of LMMs and object-centric vision. We organize the literature into four major themes: object-centric visual understanding, object-centric referring segmentation, object-centric visual editing, and object-centric visual generation. We further summarize the key modeling paradigms, learning strategies, and evaluation protocols that support these capabilities. Finally, we discuss open challenges and future directions, including robust instance permanence, fine-grained spatial control, consistent multi-step interaction, unified cross-task modeling, and reliable benchmarking under distribution shift. We hope this paper provides a structured perspective on the development of scalable, precise, and trustworthy object-centric multimodal systems.