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 …

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 …

Recent camouflaged object detection (COD) attempts to segment objects visually blended
into their surroundings, which is extremely complex and difficult in real-world scenarios …

Tracking and following objects of interest is critical to several robotics use cases, ranging
from industrial automation to logistics and warehousing, to healthcare and security. In this …

Camouflaged object detection (COD) is an important yet challenging task, with great
application values in industrial defect detection, medical care, etc. The challenges mainly …

Recent advancements in deep learning have profoundly impacted the field of animal
behavioral research, offering researchers powerful tools for understanding the complexities …

Recent camouflaged object detection (COD) approaches have been proposed to accurately
segment objects blended into surroundings. The most challenging and critical issue in COD …

Video semantic role grounding has gained substantial interest from both the academic and
industrial communities. While existing methods have demonstrated considerable …

Referring camouflaged object detection (Ref-COD) is a recently-proposed problem aiming to
segment out specified camouflaged objects matched with a textual or visual reference. This …