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 aim of camouflaged object detection (COD) is to find objects that are hidden in their
surrounding environment. Due to the factors like low illumination, occlusion, small size and …

Camouflaged object detection (COD) aims to accurately detect objects hidden in the
surrounding environment. However, the existing COD methods mainly locate camouflaged …

Camouflaged Object Detection (COD) is a challenging task in computer vision due to the
high similarity between camouflaged objects and their surroundings. Existing COD methods …

Camouflaged Object Detection (COD) is a challenging visual task due to its complex
contour, diverse scales, and high similarity to the background. Existing COD methods …

Recent years have witnessed the superiority of deep learning-based algorithms in the field
of HSI classification. However, a prerequisite for the favorable performance of these …

Different from the context-independent (CI) concepts such as human, car, and airplane,
context-dependent (CD) concepts require higher visual understanding ability, such as …

Affordance grounding aims to locate objects'“action possibilities” regions, an essential step
toward embodied intelligence. Due to the diversity of interactive affordance, ie, the …

Common mechanisms for achieving object camouflage include reducing differences and
increasing distractions. Such camouflage mechanisms hinder the object detectors to …