General and Task-Oriented Video Segmentation

TL;DR

GvSeg is a versatile video segmentation framework that unifies multiple tasks through disentangled modeling and task-specific strategies, achieving superior performance across diverse benchmarks.

Contribution

It introduces a general, architecture-agnostic framework that models segmentation targets from appearance, position, and shape, and adapts query strategies for different tasks.

Findings

Outperforms existing solutions on four video segmentation tasks

Effective across seven benchmark datasets

Successfully unifies multiple segmentation tasks in a single framework

Abstract

We present GvSeg, a general video segmentation framework for addressing four different video segmentation tasks (i.e., instance, semantic, panoptic, and exemplar-guided) while maintaining an identical architectural design. Currently, there is a trend towards developing general video segmentation solutions that can be applied across multiple tasks. This streamlines research endeavors and simplifies deployment. However, such a highly homogenized framework in current design, where each element maintains uniformity, could overlook the inherent diversity among different tasks and lead to suboptimal performance. To tackle this, GvSeg: i) provides a holistic disentanglement and modeling for segment targets, thoroughly examining them from the perspective of appearance, position, and shape, and on this basis, ii) reformulates the query initialization, matching and sampling strategies in alignment with the task-specific requirement. These architecture-agnostic innovations empower GvSeg to effectively address each unique task by accommodating the specific properties that characterize them. Extensive experiments on seven gold-standard benchmark datasets demonstrate that GvSeg surpasses all existing specialized/general solutions by a significant margin on four different video segmentation tasks.