Fadime Sener, Dibyadip Chatterjee, Angela Yao
This technical report extends our work presented in [9] with more experiments. In [9], we tackle long-term video understanding, which requires reasoning from current and past or future observations and raises several fundamental questions. How should temporal or sequential relationships be modelled? What temporal extent of information and context needs to be processed? At what temporal scale should they be derived? [9] addresses these questions with a flexible multi-granular temporal aggregation framework. In this report, we conduct further experiments with this framework on different tasks and a new dataset, EPIC-KITCHENS-100.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Activity Recognition | EPIC-KITCHENS-100 | Action@1 | 45.26 | TempAgg |
| Activity Recognition | EPIC-KITCHENS-100 | Noun@1 | 53.35 | TempAgg |
| Activity Recognition | EPIC-KITCHENS-100 | Verb@1 | 66 | TempAgg |
| Activity Recognition | EPIC-KITCHENS-100 (test) | recall@5 | 12.6 | TempAgg |
| Activity Recognition | EPIC-KITCHENS-100 | Recall@5 | 14.73 | TempAgg |
| Action Recognition | EPIC-KITCHENS-100 | Action@1 | 45.26 | TempAgg |
| Action Recognition | EPIC-KITCHENS-100 | Noun@1 | 53.35 | TempAgg |
| Action Recognition | EPIC-KITCHENS-100 | Verb@1 | 66 | TempAgg |
| Action Recognition | EPIC-KITCHENS-100 (test) | recall@5 | 12.6 | TempAgg |
| Action Recognition | EPIC-KITCHENS-100 | Recall@5 | 14.73 | TempAgg |
| Action Anticipation | EPIC-KITCHENS-100 (test) | recall@5 | 12.6 | TempAgg |
| Action Anticipation | EPIC-KITCHENS-100 | Recall@5 | 14.73 | TempAgg |
| 2D Human Pose Estimation | EPIC-KITCHENS-100 (test) | recall@5 | 12.6 | TempAgg |
| 2D Human Pose Estimation | EPIC-KITCHENS-100 | Recall@5 | 14.73 | TempAgg |
| Action Recognition In Videos | EPIC-KITCHENS-100 (test) | recall@5 | 12.6 | TempAgg |
| Action Recognition In Videos | EPIC-KITCHENS-100 | Recall@5 | 14.73 | TempAgg |