Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention

TL;DR

This paper introduces a lightweight, self-supervised video object segmentation method using deformable attention and distillation learning, achieving state-of-the-art results while being efficient enough for low-powered devices.

Contribution

The paper proposes a novel self-supervised approach with deformable attention and distillation learning, improving adaptability to temporal changes and reducing computational complexity.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Demonstrates superior memory efficiency compared to existing methods.

Effectively adapts to temporal variations in video sequences.

Abstract

Video object segmentation is a fundamental research problem in computer vision. Recent techniques have often applied attention mechanism to object representation learning from video sequences. However, due to temporal changes in the video data, attention maps may not well align with the objects of interest across video frames, causing accumulated errors in long-term video processing. In addition, existing techniques have utilised complex architectures, requiring highly computational complexity and hence limiting the ability to integrate video object segmentation into low-powered devices. To address these issues, we propose a new method for self-supervised video object segmentation based on distillation learning of deformable attention. Specifically, we devise a lightweight architecture for video object segmentation that is effectively adapted to temporal changes. This is enabled by deformable attention mechanism, where the keys and values capturing the memory of a video sequence in the attention module have flexible locations updated across frames. The learnt object representations are thus adaptive to both the spatial and temporal dimensions. We train the proposed architecture in a self-supervised fashion through a new knowledge distillation paradigm where deformable attention maps are integrated into the distillation loss. We qualitatively and quantitatively evaluate our method and compare it with existing methods on benchmark datasets including DAVIS 2016/2017 and YouTube-VOS 2018/2019. Experimental results verify the superiority of our method via its achieved state-of-the-art performance and optimal memory usage.