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 …
Segment anything
Abstract We introduce the Segment Anything (SA) project: a new task, model, and dataset for
image segmentation. Using our efficient model in a data collection loop, we built the largest …
image segmentation. Using our efficient model in a data collection loop, we built the largest …
Rethinking camouflaged object detection: Models and datasets
H Bi, C Zhang, K Wang, J Tong… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Camouflaged object detection (COD) is an emerging visual detection task, which aims to
locate and distinguish the disguised target in complex backgrounds by imitating the human …
locate and distinguish the disguised target in complex backgrounds by imitating the human …
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 …
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) …
Zoom in and out: A mixed-scale triplet network for camouflaged object detection
The recently proposed camouflaged object detection (COD) attempts to segment objects that
are visually blended into their surroundings, which is extremely complex and difficult in real …
are visually blended into their surroundings, which is extremely complex and difficult in real …
Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
Although convolutional neural networks (CNNs) have achieved great success in computer
vision, this work investigates a simpler, convolution-free backbone network useful for many …
vision, this work investigates a simpler, convolution-free backbone network useful for many …
Weakly-supervised concealed object segmentation with sam-based pseudo labeling and multi-scale feature grouping
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 …
Polyp-pvt: Polyp segmentation with pyramid vision transformers
Most polyp segmentation methods use CNNs as their backbone, leading to two key issues
when exchanging information between the encoder and decoder: 1) taking into account the …
when exchanging information between the encoder and decoder: 1) taking into account the …
Detecting camouflaged object in frequency domain
Camouflaged object detection (COD) aims to identify objects that are perfectly embedded in
their environment, which has various downstream applications in fields such as medicine …
their environment, which has various downstream applications in fields such as medicine …