EgoAdapt improves VQA on the HD-EPIC egocentric benchmark via category-conditioned routing, calibrated option scoring, and test-time consistency adaptation.
Pair: Complementarity-guided disentanglement for composed im- age retrieval
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EgoAction uses decoupled verb-noun temporal detectors on VideoMAE features and Dynamic Weighted Fusion of boundaries based on classification confidences for the EPIC-KITCHENS action detection challenge.
OmniEgo-R² is a competition system that combines domain-specific VL models with temporal normalization, capability routing, and answer calibration to reach 66.35-66.77% accuracy on the EgoCross challenge.
citing papers explorer
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EgoAdapt: A Multi-Scene Egocentric Adaptation Method for CVPR 2026 HD-EPIC VQA Challenge
EgoAdapt improves VQA on the HD-EPIC egocentric benchmark via category-conditioned routing, calibrated option scoring, and test-time consistency adaptation.
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EgoAction: Egocentric Action Composition with Reliability-Aware Temporal Fusion for the EPIC-KITCHENS Action Detection Challenge at CVPR 2026
EgoAction uses decoupled verb-noun temporal detectors on VideoMAE features and Dynamic Weighted Fusion of boundaries based on classification confidences for the EPIC-KITCHENS action detection challenge.
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OmniEgo-R$^2$: A Routed Reasoning Framework for the 1st Cross-Domain EgoCross Challenge at CVPR 2026
OmniEgo-R² is a competition system that combines domain-specific VL models with temporal normalization, capability routing, and answer calibration to reach 66.35-66.77% accuracy on the EgoCross challenge.