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

In this paper, we introduce Semantic-SAM, a universal image segmentation model to enable
segment and recognize anything at any desired granularity. Our model offers two key …

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 …

Human-centric perceptions (eg, pose estimation, human parsing, pedestrian detection,
person re-identification, etc.) play a key role in industrial applications of visual models. While …

Automatic security inspection using computer vision technology is a challenging task in real-
world scenarios due to various factors, including intra-class variance, class imbalance, and …

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 …

To address the challenging task of instance-aware human part parsing, a new bottom-up
regime is proposed to learn category-level human semantic segmentation as well as multi …

In this work, we study the challenging problem of instance-aware human body part parsing.
We introduce a new bottom-up regime which achieves the task through learning category …

Human Body Part Semantic Segmentation and Human Pose estimation are considered to
be essential for understanding human behaviours. Both of these tasks are correlated with …

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