https://github.com/zhoubolei/moments_models
Moments in Time Dataset: one million videos for event understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.
In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.
Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Moments in Time Dataset: one million videos for event understanding
Model Editing as a Robust and Denoised variant of DPO: A Case Study on Toxicity
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
DF-Net: The Digital Forensics Network for Image Forgery Detection
DF-Net: The Digital Forensics Network for Image Forgery Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.