Abstract We introduce One-shot Open Affordance Learning (OOAL) where a model is trained
with just one example per base object category but is expected to identify novel objects and …

Part-aware panoptic segmentation (PPS) requires (a) that each foreground object and
background region in an image is segmented and classified and (b) that all parts within …

Abstract Panoramic images, capturing a 360\(^\circ\) field of view (FoV), encompass
omnidirectional spatial information crucial for scene understanding. However, it is not only …

Large vision and language models learned directly through image-text associations often
lack detailed visual substantiation, whereas image segmentation tasks are treated …

We present PartGLEE, a part-level foundation model for locating and identifying both objects
and parts in images. Through a unified framework, PartGLEE accomplishes detection …

Hierarchical segmentation entails creating segmentations at varying levels of granularity.
We introduce the first hierarchical semantic segmentation dataset with subpart annotations …

Segmenting and recognizing diverse object parts is crucial in computer vision and robotics.
Despite significant progress in object segmentation, part-level segmentation remains …

Current weakly supervised semantic segmentation methods usually generate noisy pseudo-
labels. Training segmentation models with these labels tends to overfit the noise, leading to …

Affordance, defined as the potential actions that an object offers, is crucial for robotic
manipulation tasks. A deep understanding of affordance can lead to more intelligent AI …

3D part segmentation is a crucial and challenging task in 3D perception, playing a vital role
in applications such as robotics, 3D generation, and 3D editing. Recent methods harness …