FineAction: A Fine-Grained Video Dataset for Temporal Action Localization

TL;DR

FineAction introduces a large-scale, fine-grained video dataset with detailed annotations for temporal action localization, addressing limitations of coarse class datasets and enabling more precise model development.

Contribution

The paper presents a novel dataset, FineAction, with rich, fine-grained annotations and diverse action classes, facilitating improved temporal action localization research.

Findings

FineAction contains 103K instances across 106 categories.

A baseline method achieves 13.17% mAP on FineAction.

FineAction reveals the impact of fine-grained details on localization performance.

Abstract

Temporal action localization (TAL) is an important and challenging problem in video understanding. However, most existing TAL benchmarks are built upon the coarse granularity of action classes, which exhibits two major limitations in this task. First, coarse-level actions can make the localization models overfit in high-level context information, and ignore the atomic action details in the video. Second, the coarse action classes often lead to the ambiguous annotations of temporal boundaries, which are inappropriate for temporal action localization. To tackle these problems, we develop a novel large-scale and fine-grained video dataset, coined as FineAction, for temporal action localization. In total, FineAction contains 103K temporal instances of 106 action categories, annotated in 17K untrimmed videos. Compared to the existing TAL datasets, our FineAction takes distinct characteristics of fine action classes with rich diversity, dense annotations of multiple instances, and co-occurring actions of different classes, which introduces new opportunities and challenges for temporal action localization. To benchmark FineAction, we systematically investigate the performance of several popular temporal localization methods on it, and deeply analyze the influence of fine-grained instances in temporal action localization. As a minor contribution, we present a simple baseline approach for handling the fine-grained action detection, which achieves an mAP of 13.17% on our FineAction. We believe that FineAction can advance research of temporal action localization and beyond.