Referring Segmentation in Images and Videos with Cross-Modal Self-Attention Network

TL;DR

This paper introduces a novel cross-modal self-attention network for referring segmentation in images and videos, effectively capturing long-range dependencies and integrating multi-level visual features to improve segmentation accuracy.

Contribution

The paper proposes a cross-modal self-attention module, a gated multi-level fusion module, and a cross-frame self-attention module, advancing the state-of-the-art in referring segmentation tasks.

Findings

Outperforms existing methods on benchmark datasets.

Effectively captures long-range dependencies between language and visual features.

Successfully extends to video segmentation with temporal information integration.

Abstract

We consider the problem of referring segmentation in images and videos with natural language. Given an input image (or video) and a referring expression, the goal is to segment the entity referred by the expression in the image or video. In this paper, we propose a cross-modal self-attention (CMSA) module to utilize fine details of individual words and the input image or video, which effectively captures the long-range dependencies between linguistic and visual features. Our model can adaptively focus on informative words in the referring expression and important regions in the visual input. We further propose a gated multi-level fusion (GMLF) module to selectively integrate self-attentive cross-modal features corresponding to different levels of visual features. This module controls the feature fusion of information flow of features at different levels with high-level and low-level semantic information related to different attentive words. Besides, we introduce cross-frame self-attention (CFSA) module to effectively integrate temporal information in consecutive frames which extends our method in the case of referring segmentation in videos. Experiments on benchmark datasets of four referring image datasets and two actor and action video segmentation datasets consistently demonstrate that our proposed approach outperforms existing state-of-the-art methods.