Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection
SAM is a segmentation model recently released by Meta AI Research and has been gaining attention quickly due to its impressive performance in generic object segmentation. However, its ability to generalize to specific scenes such as camouflaged scenes is still unknown. Camouflaged object detection (COD) involves identifying objects that are seamlessly integrated into their surroundings and has numerous practical applications in fields such as medicine, art, and agriculture. In this study, we try to ask if SAM can address the COD task and evaluate the performance of SAM on the COD benchmark by employing maximum segmentation evaluation and camouflage location evaluation. We also compare SAM's performance with 22 state-of-the-art COD methods. Our results indicate that while SAM shows promise in generic object segmentation, its performance on the COD task is limited. This presents an opportunity for further research to explore how to build a stronger SAM that may address the COD task. The results of this paper are provided in \url{https://github.com/luckybird1994/SAMCOD}.
Code (1)
Tasks
Objectobject-detectionObject DetectionSegmentationSemantic SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
When SAM Meets Shadow Detection
As a promptable generic object segmentation model, segment anything model (SAM) has recently attracted significant attention, and also demonstrates its powerful performance. Nevertheless, it still meets its Waterloo when…
Image SegmentationMedical Image SegmentationObjectobject-detection+4When SAM2 Meets Video Camouflaged Object Segmentation: A Comprehensive Evaluation and Adaptation
This study investigates the application and performance of the Segment Anything Model 2 (SAM2) in the challenging task of video camouflaged object segmentation (VCOS). VCOS involves detecting objects that blend seamlessl…
Camouflaged Object SegmentationSemantic SegmentationCamouflage Anything: Learning to Hide using Controlled Out-painting and Representation Engineering
In this work, we introduce Camouflage Anything, a novel and robust approach to generate camouflaged datasets. To the best of our knowledge, we are the first to apply Controlled Out-painting and Representation Enginee…
Camouflaged Object SegmentationObjectSemantic SegmentationCamSAM2: Segment Anything Accurately in Camouflaged Videos
Video camouflaged object segmentation (VCOS), aiming at segmenting camouflaged objects that seamlessly blend into their environment, is a fundamental vision task with various real-world applications. With the release of …
Camouflaged Object SegmentationObjectSemantic SegmentationVideo Segmentation+1Evaluating SAM2's Role in Camouflaged Object Detection: From SAM to SAM2
The Segment Anything Model (SAM), introduced by Meta AI Research as a generic object segmentation model, quickly garnered widespread attention and significantly influenced the academic community. To extend its applicatio…
Image Segmentationobject-detectionObject DetectionSegmentation+1