The Kinetics Human Action Video Dataset
원문 초록
We describe the DeepMind Kinetics human action video dataset. The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 10s and is taken from a different YouTube video. The actions are human focussed and cover a broad range of classes including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands. We describe the statistics of the dataset, how it was collected, and give some baseline performance figures for neural network architectures trained and tested for human action classification on this dataset. We also carry out a preliminary analysis of whether imbalance in the dataset leads to bias in the classifiers.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Cross-Block Fine-Grained Semantic Cascade for Skeleton-Based Sports Action Recognition
Support Vector Machines with Time Series Distance Kernels for Action Classification
From Play to Replay: Composed Video Retrieval for Temporally Fine-Grained Videos
Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition
Make Your Training Flexible: Towards Deployment-Efficient Video Models
Using Instruction-Tuned Large Language Models to Identify Indicators of Vulnerability in Police Incident Narratives
토론
이 논문과 연결된 포럼 글이 아직 없습니다.