CamoFormer: Masked Separable Attention for Camouflaged Object Detection

• URL: http://arxiv.org/abs/2212.06570v1
• Date: Sat, 10 Dec 2022 10:03:27 GMT
• Title: CamoFormer: Masked Separable Attention for Camouflaged Object Detection
• Authors: Bowen Yin and Xuying Zhang and Qibin Hou and Bo-Yuan Sun and Deng-Ping Fan and Luc Van Gool
• Abstract summary: We present a simple masked separable attention (MSA) for camouflaged object detection. We first separate the multi-head self-attention into three parts, which are responsible for distinguishing the camouflaged objects from the background using different mask strategies. We propose to capture high-resolution semantic representations progressively based on a simple top-down decoder with the proposed MSA to attain precise segmentation results.
• Score: 94.2870722866853
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: How to identify and segment camouflaged objects from the background is challenging. Inspired by the multi-head self-attention in Transformers, we present a simple masked separable attention (MSA) for camouflaged object detection. We first separate the multi-head self-attention into three parts, which are responsible for distinguishing the camouflaged objects from the background using different mask strategies. Furthermore, we propose to capture high-resolution semantic representations progressively based on a simple top-down decoder with the proposed MSA to attain precise segmentation results. These structures plus a backbone encoder form a new model, dubbed CamoFormer. Extensive experiments show that CamoFormer surpasses all existing state-of-the-art methods on three widely-used camouflaged object detection benchmarks. There are on average around 5% relative improvements over previous methods in terms of S-measure and weighted F-measure.

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