Object functionalization is cast as neural graph completion over a functional graph of parts, contacts, and motions, followed by geometry realization that also rectifies erroneous motions, demonstrated on furniture with a new paired dataset.
IEEE transactions on pattern analysis and machine intelligence , volume=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
LightAVSeg decouples semantic filtering and spatial grounding to achieve linear-cost cross-modal interaction in audio-visual segmentation, reaching 50.4 mIoU on MS3 with 20.5M parameters as a new lightweight state-of-the-art.
ZScribbleSeg maximizes scribble supervision with efficient annotation forms, spatial regularization, and EM-estimated class ratios to deliver competitive performance on six medical segmentation tasks without full labels.
citing papers explorer
-
Functionalization via Structure Completion and Motion Rectification
Object functionalization is cast as neural graph completion over a functional graph of parts, contacts, and motions, followed by geometry realization that also rectifies erroneous motions, demonstrated on furniture with a new paired dataset.
-
LightAVSeg: Lightweight Audio-Visual Segmentation
LightAVSeg decouples semantic filtering and spatial grounding to achieve linear-cost cross-modal interaction in audio-visual segmentation, reaching 50.4 mIoU on MS3 with 20.5M parameters as a new lightweight state-of-the-art.
-
ZScribbleSeg: A comprehensive segmentation framework with modeling of efficient annotation and maximization of scribble supervision
ZScribbleSeg maximizes scribble supervision with efficient annotation forms, spatial regularization, and EM-estimated class ratios to deliver competitive performance on six medical segmentation tasks without full labels.