视频理解


2023-01-18 更新

Building Scalable Video Understanding Benchmarks through Sports

Authors:Aniket Agarwal, Alex Zhang, Karthik Narasimhan, Igor Gilitschenski, Vishvak Murahari, Yash Kant

Existing benchmarks for evaluating long video understanding falls short on multiple aspects, either lacking in scale or quality of annotations. These limitations arise from the difficulty in collecting dense annotations for long videos (e.g. actions, dialogues, etc.), which are often obtained by manually labeling many frames per second. In this work, we introduce an automated Annotation and Video Stream Alignment Pipeline (abbreviated ASAP). We demonstrate the generality of ASAP by aligning unlabeled videos of four different sports (Cricket, Football, Basketball, and American Football) with their corresponding dense annotations (i.e. commentary) freely available on the web. Our human studies indicate that ASAP can align videos and annotations with high fidelity, precision, and speed. We then leverage ASAP scalability to create LCric, a large-scale long video understanding benchmark, with over 1000 hours of densely annotated long Cricket videos (with an average sample length of 50 mins) collected at virtually zero annotation cost. We benchmark and analyze state-of-the-art video understanding models on LCric through a large set of compositional multi-choice and regression queries. We establish a human baseline that indicates significant room for new research to explore.
PDF

点此查看论文截图

文章作者: 木子已
版权声明: 本博客所有文章除特別声明外,均采用 CC BY 4.0 许可协议。转载请注明来源 木子已 !
  目录