ViLLa: Video Reasoning Segmentation with Large Language Model

TL;DR

ViLLa introduces a novel approach combining dynamic context encoding, hierarchical temporal modeling, and adaptive segmentation to significantly improve video reasoning segmentation in complex, real-world scenarios using large language models.

Contribution

The paper presents ViLLa, a new framework that enhances video reasoning segmentation by addressing complex scenarios with innovative modules and introduces a new benchmark dataset.

Findings

Achieves state-of-the-art results on multiple benchmarks.

Effectively handles long videos with complex object interactions.

Demonstrates improved reasoning accuracy in real-world scenes.

Abstract

Recent efforts in video reasoning segmentation (VRS) integrate large language models (LLMs) with perception models to localize and track objects via textual instructions, achieving barely satisfactory results in simple scenarios. However, they struggled to discriminate and deduce the objects from user queries in more real-world scenes featured by long durations, multiple objects, rapid motion, and heavy occlusions. In this work, we analyze the underlying causes of these limitations, and present ViLLa: Video reasoning segmentation with Large Language Model. Remarkably, our ViLLa manages to tackle these challenges through multiple core innovations: (1) a context synthesizer that dynamically encodes the user intent with video contexts for accurate reasoning, resolving ambiguities in complex queries, and (2) a hierarchical temporal synchronizer that disentangles multi-object interactions across complex temporal scenarios by modelling multi-object interactions at local and global temporal scales. To enable efficient processing of long videos, ViLLa incorporates (3) a key segment sampler that adaptively partitions long videos into shorter but semantically dense segments for less redundancy. What's more, to promote research in this unexplored area, we construct a VRS benchmark, VideoReasonSeg, featuring different complex scenarios. Our model also exhibits impressive state-of-the-art results on VideoReasonSeg, Ref-YouTube-VOS, Ref-DAVIS17, MeViS, and ReVOS. Both quantitative and qualitative experiments demonstrate that our method effectively enhances video reasoning segmentation capabilities for multimodal LLMs. The code and dataset will be available at https://github.com/rkzheng99/ViLLa.