mlsanityData to 7 Oct 2026

A Better Baseline for AVA

arXiv 1807.10066 ↗First posted 26 Jul 2018cs.CVcited by 16

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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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jul 2018.

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Citing papers per year: 2021: 9; 2022: 4; 2023: 2; 2024: 0; 2025: 1.

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