CamoFormer : Masked Separable Attention for Camouflaged Object Detection
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, whic...
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 46(2024), 12 vom: 05. Dez., Seite 10362-10374 |
|---|---|
| Auteur principal: | |
| Autres auteurs: | , , , , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2024
|
| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence |
| Sujets: | Journal Article |
| Résumé: | 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 achieves new state-of-the-art performance on three widely-used camouflaged object detection benchmarks. To better evaluate the performance of the proposed CamoFormer around the border regions, we propose to use two new metrics, i.e., BR-M and BR-F. There are on average ∼ 5% relative improvements over previous methods in terms of S-measure and weighted F-measure |
|---|---|
| Description: | Date Revised 08.11.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1939-3539 |
| DOI: | 10.1109/TPAMI.2024.3438565 |