FROST-STA ranks second in the Ego4D Short-Term Object Interaction Anticipation challenge with 5.13 mAP by adapting frozen V-JEPA features with object-centric heads and ensembling.
JFAA: Technical Report for the EPIC-KITCHENS-100 Action Anticipation Challenge at EgoVis 2026
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abstract
We propose JFAA, a JEPA-based Future Action Anticipation method for the EPIC-KITCHENS-100 (EK-100) Action Anticipation task. Inspired by the representation learning and future prediction ability of V-JEPA 2.1, JFAA uses a frozen encoder and predictor to extract observed context features and near-future latent tokens. A lightweight attentive probe is then trained to predict verb, noun, and action logits with separate task queries. To improve robustness, we further build a field-aware ensemble over selected epoch-level predictions, allowing each output field to benefit from its most reliable candidates. Experimental results on the official challenge server show that JFAA achieves first place in the EgoVis 2026 EK-100 Action Anticipation Challenge. Our code will be released at https://github.com/CorrineQiu/JFAA.
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
TAP-JEPA applies frozen V-JEPA features, latent future prediction, and two-stage fusion of attentive probes to reach 27.91% MT5R and second place on the EK-100 action anticipation leaderboard.
citing papers explorer
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FROST-STA: Frozen Dense Features for the Ego4D Short-Term Object Interaction Anticipation
FROST-STA ranks second in the Ego4D Short-Term Object Interaction Anticipation challenge with 5.13 mAP by adapting frozen V-JEPA features with object-centric heads and ensembling.
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TAP-JEPA: Frozen Future-Latent Probing and Two-Stage Score Fusion for EPIC-KITCHENS-100 Action Anticipation
TAP-JEPA applies frozen V-JEPA features, latent future prediction, and two-stage fusion of attentive probes to reach 27.91% MT5R and second place on the EK-100 action anticipation leaderboard.