# ZARRIO @ Ego4D Short Term Object Interaction Anticipation Challenge: Leveraging Affordances and Attention-based models for STA

arXiv: https://arxiv.org/abs/2407.04369 · HTML: https://mlsanity.com/paper/2407.04369/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 14 references that are in the map.
- First posted on arXiv 5 Jul 2024.

## Builds on (most cited first, 13 of 13)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [EGO-TOPO: Environment Affordances from Egocentric Video](https://mlsanity.com/paper/2001.04583/) (2020)
- [Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos](https://mlsanity.com/paper/2204.01696/) (2022)
- [InternVideo-Ego4D: A Pack of Champion Solutions to Ego4D Challenges](https://mlsanity.com/paper/2211.09529/) (2022)
- [StillFast: An End-to-End Approach for Short-Term Object Interaction Anticipation](https://mlsanity.com/paper/2304.03959/) (2023)
- [Summarize the Past to Predict the Future: Natural Language Descriptions of Context Boost Multimodal Object Interaction Anticipation](https://mlsanity.com/paper/2301.09209/) (2023)
- [Leveraging Next-Active Objects for Context-Aware Anticipation in Egocentric Videos](https://mlsanity.com/paper/2308.08303/) (2023)
- [AFF-ttention! Affordances and Attention models for Short-Term Object Interaction Anticipation](https://mlsanity.com/paper/2406.01194/) (2024)
- [Enhancing Next Active Object-based Egocentric Action Anticipation with Guided Attention](https://mlsanity.com/paper/2305.12953/) (2023)

## Abstract (arXiv)

Short-Term object-interaction Anticipation (STA) consists of detecting the location of the next-active objects, the noun and verb categories of the interaction, and the time to contact from the observation of egocentric video. We propose STAformer, a novel attention-based architecture integrating frame-guided temporal pooling, dual image-video attention, and multi-scale feature fusion to support STA predictions from an image-input video pair. Moreover, we introduce two novel modules to ground STA predictions on human behavior by modeling affordances. First, we integrate an environment affordance model which acts as a persistent memory of interactions that can take place in a given physical scene. Second, we predict interaction hotspots from the observation of hands and object trajectories, increasing confidence in STA predictions localized around the hotspot. On the test set, our results obtain a final 33.5 N mAP, 17.25 N+V mAP, 11.77 N+δ mAP and 6.75 Overall top-5 mAP metric when trained on the v2 training dataset.