Human parsing aims to partition humans in image or video into multiple pixel-level semantic
parts. In the last decade, it has gained significantly increased interest in the computer vision …

The simultaneous recognition of multiple objects in one image remains a challenging task,
spanning multiple events in the recognition field such as various object scales, inconsistent …

RGB-D salient object detection (SOD) aims to segment the most attractive objects in a pair of
cross-modal RGB and depth images. Currently, most existing RGB-D SOD methods focus on …

Salient object detection (SOD) has made great progress, but most of existing SOD methods
focus more on performance than efficiency. Besides, the U-shape structure exists some …

Current high-performance semantic segmentation models are purely data-driven sub-
symbolic approaches and blind to the structured nature of the visual world. This is in stark …

Scene parsing is a fundamental task in computer vision. Various RGB-D (color and depth)
scene parsing methods based on fully convolutional networks have achieved excellent …

In this work, we introduce the new scene understanding task of Part-aware Panoptic
Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and …

Humans are able to recognize structured relations in observation, allowing us to decompose
complex scenes into simpler parts and abstract the visual world at multiple levels. However …

Abstract Panoptic Part Segmentation (PPS) aims to unify panoptic segmentation and part
segmentation into one task. Previous work mainly utilizes separated approaches to handle …

Multi-modal large language models (MLLMs) have made significant strides in various visual
understanding tasks. However, the majority of these models are constrained to process low …