# A Better Baseline for AVA

arXiv: https://arxiv.org/abs/1807.10066 · HTML: https://mlsanity.com/paper/1807.10066/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2018.

## Built on by (most cited first, 16 of 16)

- [TubeR: Tubelet Transformer for Video Action Detection](https://mlsanity.com/paper/2104.00969/) (2021)
- [ROAD: The ROad event Awareness Dataset for Autonomous Driving](https://mlsanity.com/paper/2102.11585/) (2021)
- [JRDB-Act: A Large-scale Dataset for Spatio-temporal Action, Social Group and Activity Detection](https://mlsanity.com/paper/2106.08827/) (2021)
- [Deep Learning-based Action Detection in Untrimmed Videos: A Survey](https://mlsanity.com/paper/2110.00111/) (2021)
- [Attend What You Need: Motion-Appearance Synergistic Networks for Video Question Answering](https://mlsanity.com/paper/2106.10446/) (2021)
- [SVIP: Sequence VerIfication for Procedures in Videos](https://mlsanity.com/paper/2112.06447/) (2021)
- [Spatio-Temporal Action Detection Under Large Motion](https://mlsanity.com/paper/2209.02250/) (2022)
- [A Simple and Efficient Pipeline to Build an End-to-End Spatial-Temporal Action Detector](https://mlsanity.com/paper/2206.03064/) (2022)
- [Game State and Spatio-temporal Action Detection in Soccer using Graph Neural Networks and 3D Convolutional Networks](https://mlsanity.com/paper/2502.15462/) (2025)
- [A Survey on Deep Learning-based Spatio-temporal Action Detection](https://mlsanity.com/paper/2308.01618/) (2023)
- [CycleACR: Cycle Modeling of Actor-Context Relations for Video Action Detection](https://mlsanity.com/paper/2303.16118/) (2023)
- [Projection: A Mechanism for Human-like Reasoning in Artificial Intelligence](https://mlsanity.com/paper/2103.13512/) (2021)
- [Spatio-Temporal Context for Action Detection](https://mlsanity.com/paper/2106.15171/) (2021)
- [Hierarchical Graph-RNNs for Action Detection of Multiple Activities](https://mlsanity.com/paper/2101.08581/) (2021)
- [Scalable Temporal Localization of Sensitive Activities in Movies and TV Episodes](https://mlsanity.com/paper/2206.08429/) (2022)
- [Actor-identified Spatiotemporal Action Detection -- Detecting Who Is Doing What in Videos](https://mlsanity.com/paper/2208.12940/) (2022)

## Abstract (arXiv)

We introduce a simple baseline for action localization on the AVA dataset. The model builds upon the Faster R-CNN bounding box detection framework, adapted to operate on pure spatiotemporal features - in our case produced exclusively by an I3D model pretrained on Kinetics. This model obtains 21.9% average AP on the validation set of AVA v2.1, up from 14.5% for the best RGB spatiotemporal model used in the original AVA paper (which was pretrained on Kinetics and ImageNet), and up from 11.3 of the publicly available baseline using a ResNet101 image feature extractor, that was pretrained on ImageNet. Our final model obtains 22.8%/21.9% mAP on the val/test sets and outperforms all submissions to the AVA challenge at CVPR 2018.