根据视频难易度自动选择标注类型，降低动作检测标注成本
OmViD: Omni-supervised active learning for video action detection
- 按视频难度动态选择标签、框、涂鸦等不同标注方式
- 在UCF101-24和JHMDB-21上减少标注量超60%且性能损失<3%
- 适合需要低成本标注的动作检测项目
视频动作检测需密集的时空标注，获取成本高且困难。真实视频难度各异，无需统一标注强度。本文分析不同样本适用的标注类型及其对检测效果的影响，聚焦两大问题：如何为视频获取不同级别的标注，以及如何从不同标注类型中学习动作检测。研究涵盖视频级标签、点、涂鸦、边界框和像素级掩码。首先提出简单主动学习策略，评估每段视频所需标注类型；随后引入新颖的时空3D超像素方法，从这些标注生成伪标签，实现有效训练。在UCF101-24和JHMDB-21数据集上验证，显著降低标注成本，性能损失小于3%。
原文摘要 · Abstract (English)
Video action detection requires dense spatio-temporal annotations, which are both challenging and expensive to obtain. However, real-world videos often vary in difficulty and may not require the same level of annotation. This paper analyzes the appropriate annotation types for each sample and their impact on spatio-temporal video action detection. It focuses on two key aspects: 1) how to obtain varying levels of annotation for videos, and 2) how to learn action detection from different annotation types. The study explores video-level tags, points, scribbles, bounding boxes, and pixel-level masks. First, a simple active learning strategy is proposed to estimate the necessary annotation type for each video. Then, a novel spatio-temporal 3D-superpixel approach is introduced to generate pseudo-labels from these annotations, enabling effective training. The approach is validated on UCF101-24 and JHMDB-21 datasets, significantly cutting annotation costs with minimal performance loss.
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