EgoAdapt improves VQA on the HD-EPIC egocentric benchmark via category-conditioned routing, calibrated option scoring, and test-time consistency adaptation.
Retrack: Evidence-driven dual-stream directional anchor calibration network for com- posed video retrieval
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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.
TempRet enhances a CLIP dual-encoder with temporal modeling and two-stage reranking to report 67.97% mAP and 82.92% nDCG on the EK-100 MIR benchmark.
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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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.
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TempRet: Temporal Enhancement and Two-Stage Reranking for CVPR 2026 EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge
TempRet enhances a CLIP dual-encoder with temporal modeling and two-stage reranking to report 67.97% mAP and 82.92% nDCG on the EK-100 MIR benchmark.