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.
@article{arxiv.2605.20904,
title = {JFAA: Technical Report for the EPIC-KITCHENS-100 Action Anticipation Challenge at EgoVis 2026},
author = {Qiaohui Chu and Haoyu Zhang and Yisen Feng and Meng Liu and Weili Guan and Dongmei Jiang and Liqiang Nie},
journal= {arXiv preprint arXiv:2605.20904},
year = {2026}
}
Comments
The champion solution for the EPIC-KITCHENS-100 Action Anticipation Challenge at the CVPR EgoVis Workshop 2026