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.
International Journal of Computer Vision , volume=
4 Pith papers cite this work. Polarity classification is still indexing.
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LSAMD searches a multi-dataset super Ans-Net to extract frequently selected base blocks as learngenes that initialize variable-sized Des-Nets with performance comparable to full pretrain-finetune at lower storage and training cost.
V-JEPA models trained only on feature prediction from 2 million public videos achieve 81.9% on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet-1K using frozen ViT-H/16 backbones.
A semi-supervised pipeline applies UniMatch V2 to the WeatherProof dataset by treating degraded images as unlabeled data plus test-time augmentation for semantic segmentation in adverse weather.
citing papers explorer
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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.
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Learngene Search Across Multiple Datasets for Building Variable-Sized Models
LSAMD searches a multi-dataset super Ans-Net to extract frequently selected base blocks as learngenes that initialize variable-sized Des-Nets with performance comparable to full pretrain-finetune at lower storage and training cost.
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Revisiting Feature Prediction for Learning Visual Representations from Video
V-JEPA models trained only on feature prediction from 2 million public videos achieve 81.9% on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet-1K using frozen ViT-H/16 backbones.
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A Robust Semantic Segmentation Pipeline for the CVPR 2026 8th UG2+ Challenge Track 2
A semi-supervised pipeline applies UniMatch V2 to the WeatherProof dataset by treating degraded images as unlabeled data plus test-time augmentation for semantic segmentation in adverse weather.