CamoFormer: Masked Separable Attention for Camouflaged Object Detection
It addresses the problem of identifying camouflaged objects in images for computer vision applications, representing an incremental advance with specific performance gains.
The paper tackles camouflaged object detection by proposing CamoFormer, a model using masked separable attention and a top-down decoder, which achieves state-of-the-art results with around 5% relative improvements in S-measure and weighted F-measure on three benchmarks.
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.