Situational Perception Guided Image Matting
Bo Xu, Jiake Xie, Han Huang, Ziwen Li, Cheng Lu, Yong, Tang, Yandong Guo

TL;DR
This paper introduces a novel image matting approach that leverages situational perception and visual-to-textual guidance to improve saliency detection and reduce annotation bias, outperforming existing methods.
Contribution
The proposed SPG-IM method integrates a Semantic Transformation module and an Adaptive Focal Transformation network to enhance global and local saliency in image matting.
Findings
Outperforms state-of-the-art image matting methods.
Effectively captures inter-object and environment saliency.
Reduces subjective bias in matting annotations.
Abstract
Most automatic matting methods try to separate the salient foreground from the background. However, the insufficient quantity and subjective bias of the current existing matting datasets make it difficult to fully explore the semantic association between object-to-object and object-to-environment in a given image. In this paper, we propose a Situational Perception Guided Image Matting (SPG-IM) method that mitigates subjective bias of matting annotations and captures sufficient situational perception information for better global saliency distilled from the visual-to-textual task. SPG-IM can better associate inter-objects and object-to-environment saliency, and compensate the subjective nature of image matting and its expensive annotation. We also introduce a textual Semantic Transformation (TST) module that can effectively transform and integrate the semantic feature stream to guide the…
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Taxonomy
TopicsImage Enhancement Techniques · Visual Attention and Saliency Detection · Advanced Image Fusion Techniques
