# JFAA: Technical Report for the EPIC-KITCHENS-100 Action Anticipation Challenge at EgoVis 2026

arXiv: https://arxiv.org/abs/2605.20904 · HTML: https://mlsanity.com/paper/2605.20904/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 15 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 11 of 11)

- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Rescaling Egocentric Vision](https://mlsanity.com/paper/2006.13256/) (2020)
- [The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines](https://mlsanity.com/paper/2005.00343/) (2020)
- [V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning](https://mlsanity.com/paper/2603.14482/) (2026)
- [Spatial Understanding from Videos: Structured Prompts Meet Simulation Data](https://mlsanity.com/paper/2506.03642/) (2025)
- [Exo2Ego: Exocentric Knowledge Guided MLLM for Egocentric Video Understanding](https://mlsanity.com/paper/2503.09143/) (2025)
- [Intention-Guided Cognitive Reasoning for Egocentric Long-Term Action Anticipation](https://mlsanity.com/paper/2508.01742/) (2025)
- [Technical Report for Ego4D Long-Term Action Anticipation Challenge 2025](https://mlsanity.com/paper/2506.02550/) (2025)
- [HCQA-1.5 @ Ego4D EgoSchema Challenge 2025](https://mlsanity.com/paper/2505.20644/) (2025)
- [OSGNet @ Ego4D Episodic Memory Challenge 2025](https://mlsanity.com/paper/2506.03710/) (2025)

## Built on by (most cited first, 2 of 2)

- [TAP-JEPA: Frozen Future-Latent Probing and Two-Stage Score Fusion for EPIC-KITCHENS-100 Action Anticipation](https://mlsanity.com/paper/2606.00662/) (2026)
- [FROST-STA: Frozen Dense Features for the Ego4D Short-Term Object Interaction Anticipation](https://mlsanity.com/paper/2606.00694/) (2026)

## Abstract (arXiv)

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.