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 …
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 …
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 …
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 …
Strategic preys make acute predators: Enhancing camouflaged object detectors by generating camouflaged objects
Camouflaged object detection (COD) is the challenging task of identifying camouflaged
objects visually blended into surroundings. Albeit achieving remarkable success, existing …
objects visually blended into surroundings. Albeit achieving remarkable success, existing …
Fastinst: A simple query-based model for real-time instance segmentation
Recent attention in instance segmentation has focused on query-based models. Despite
being non-maximum suppression (NMS)-free and end-to-end, the superiority of these …
being non-maximum suppression (NMS)-free and end-to-end, the superiority of these …
Camouflaged instance segmentation via explicit de-camouflaging
Abstract Camouflaged Instance Segmentation (CIS) aims at predicting the instance-level
masks of camouflaged objects, which are usually the animals in the wild adapting their …
masks of camouflaged objects, which are usually the animals in the wild adapting their …
UTLNet: Uncertainty-aware transformer localization network for RGB-depth mirror segmentation
Mirror segmentation, an emerging discipline in the field of computer vision, involves the
identification and marking of mirrors in an image. Current mirror segmentation methods rely …
identification and marking of mirrors in an image. Current mirror segmentation methods rely …
A simple yet effective network based on vision transformer for camouflaged object and salient object detection
Camouflaged object detection (COD) and salient object detection (SOD) are two distinct yet
closely-related computer vision tasks widely studied during the past decades. Though …
closely-related computer vision tasks widely studied during the past decades. Though …
Mdqe: Mining discriminative query embeddings to segment occluded instances on challenging videos
While impressive progress has been achieved, video instance segmentation (VIS) methods
with per-clip input often fail on challenging videos with occluded objects and crowded …
with per-clip input often fail on challenging videos with occluded objects and crowded …