Advances in deep concealed scene understanding
Concealed scene understanding (CSU) is a hot computer vision topic aiming to perceive
objects exhibiting camouflage. The current boom in terms of techniques and applications …
objects exhibiting camouflage. The current boom in terms of techniques and applications …
Camouflaged object detection with feature decomposition and edge reconstruction
Camouflaged object detection (COD) aims to address the tough issue of identifying
camouflaged objects visually blended into the surrounding backgrounds. COD is a …
camouflaged objects visually blended into the surrounding backgrounds. COD is a …
Weakly-supervised concealed object segmentation with sam-based pseudo labeling and multi-scale feature grou**
Abstract Weakly-Supervised Concealed Object Segmentation (WSCOS) aims to segment
objects well blended with surrounding environments using sparsely-annotated data for …
objects well blended with surrounding environments using sparsely-annotated data for …
Segment anything is not always perfect: An investigation of sam on different real-world applications
Abstract Recently, Meta AI Research approaches a general, promptable segment anything
model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B) …
model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B) …
A systematic review of image-level camouflaged object detection with deep learning
Y Liang, G Qin, M Sun, X Wang, J Yan, Z Zhang - Neurocomputing, 2024 - Elsevier
Camouflaged object detection (COD) aims to search and identify disguised objects that are
hidden in their surrounding environment, thereby deceiving the human visual system. As an …
hidden in their surrounding environment, thereby deceiving the human visual system. As an …
Feature shrinkage pyramid for camouflaged object detection with transformers
Vision transformers have recently shown strong global context modeling capabilities in
camouflaged object detection. However, they suffer from two major limitations: less effective …
camouflaged object detection. However, they suffer from two major limitations: less effective …
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 …
attention quickly due to its impressive performance in generic object segmentation …
Pixels, regions, and objects: Multiple enhancement for salient object detection
Salient object detection (SOD) aims to mimic the human visual system (HVS) and cognition
mechanisms to identify and segment salient objects. However, due to the complexity of …
mechanisms to identify and segment salient objects. However, due to the complexity of …
Deep gradient learning for efficient camouflaged object detection
This paper introduces deep gradient network (DGNet), a novel deep framework that exploits
object gradient supervision for camouflaged object detection (COD). It decouples the task …
object gradient supervision for camouflaged object detection (COD). It decouples the task …
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 …
Inspired by the multi-head self-attention in Transformers, we present a simple masked …