Evaluating SAM2's Role in Camouflaged Object Detection: From SAM to SAM2

L Tang, B Li - arXiv preprint arXiv:2407.21596, 2024 - arxiv.org
The Segment Anything Model (SAM), introduced by Meta AI Research as a generic object
segmentation model, quickly garnered widespread attention and significantly influenced the …

When SAM2 Meets Video Camouflaged Object Segmentation: A Comprehensive Evaluation and Adaptation

Y Zhou, G Sun, Y Li, L Benini, E Konukoglu - arXiv preprint arXiv …, 2024 - arxiv.org
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 …

Towards Real Zero-Shot Camouflaged Object Segmentation without Camouflaged Annotations

C Lei, J Fan, X Li, T Xiang, A Li, C Zhu… - arXiv preprint arXiv …, 2024 - arxiv.org
Camouflaged Object Segmentation (COS) faces significant challenges due to the scarcity of
annotated data, where meticulous pixel-level annotation is both labor-intensive and costly …

A survey of camouflaged object detection and beyond

F Xiao, S Hu, Y Shen, C Fang, J Huang, C He… - arXiv preprint arXiv …, 2024 - arxiv.org
Camouflaged Object Detection (COD) refers to the task of identifying and segmenting
objects that blend seamlessly into their surroundings, posing a significant challenge for …

BehAV: Behavioral Rule Guided Autonomy Using VLMs for Robot Navigation in Outdoor Scenes

K Weerakoon, M Elnoor, G Seneviratne… - arXiv preprint arXiv …, 2024 - arxiv.org
We present BehAV, a novel approach for autonomous robot navigation in outdoor scenes
guided by human instructions and leveraging Vision Language Models (VLMs). Our method …