Papers with Code 아카이브
Video Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 0
- 보존된 평가 기록
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A crucial task of Video Understanding is to recognise and localise (in space and time) different actions or events appearing in the video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yuweijiang/HybridNet
Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning
https://github.com/zhengyuzhao/koniq-pytorch
KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment
https://github.com/zhoubolei/moments_models
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding
https://github.com/zibojia/rslad
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/zjfheart/Friendly-Adversarial-Training
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/zwtu/TokShift-Transformer-Paddle
https://github.com/zwx8981/tcsvt-2022-bvqa
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception
https://github.com/zzy-zhou/renet
Event-Free Moving Object Segmentation from Moving Ego Vehicle
https://github.com/zzz2010/starganv2_paddle
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.