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 …

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 …

Camouflaged object detection (COD) aims to detect/segment camouflaged objects
embedded in the environment, which has attracted increasing attention over the past …

We present OSFormer, the first one-stage transformer framework for camouflaged instance
segmentation (CIS). OSFormer is based on two key designs. First, we design a location …

Aleatoric uncertainty captures noise within the observations. For camouflaged object
detection, due to similar appearance of the camouflaged foreground and the background, it's …

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 …

The proliferation of deepfake media is raising concerns among the public and relevant
authorities. It has become essential to develop countermeasures against forged faces in …

The goal of Camouflaged object detection (COD) is to detect objects that are visually
embedded in their surroundings. Existing COD methods only focus on detecting …

Recently, indiscernible scene understanding has attracted a lot of attention in the vision
community. We further advance the frontier of this field by systematically studying a new …

Dense image segmentation tasks eg, semantic, panoptic) are useful for image editing, but
existing methods can hardly generalize well in an in-the-wild setting where there are …