https://github.com/MS-P3/code4/tree/main/SqueezeTime
No Time to Waste: Squeeze Time into Channel for Mobile Video Understanding
Papers with Code 아카이브
Video Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 7
- 평가표
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- 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/MS-P3/code7/tree/main/videomae
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/Meghraj-Webllisto/stylegan
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/Mind23-2/MindCode-101/tree/main/nima
https://github.com/Mind23-2/MindCode-11
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/Mind23-2/MindCode-3/tree/main/nima
https://github.com/MindCode-4/code-1/tree/main/videomae
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MindCode-4/code-5/tree/main/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/MindSpore-paper-code-2/code3/tree/main/tsm
TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Device
https://github.com/MindSpore-paper-code-3/code1/tree/main/AttentionCluster
Learnable pooling with Context Gating for video classification
https://github.com/MindSpore-paper-code-3/code2/tree/main/DDM
Progressive Attention on Multi-Level Dense Difference Maps for Generic Event Boundary Detection
https://github.com/MindSpore-paper-code-3/code6/tree/main/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/MindSpore-paper-code-3/code8/tree/main/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/MindSpore-scientific-2/code-14/tree/main/llava_next
https://github.com/MindSpore-scientific-2/code-8/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/MindSpore-scientific/code-1/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/MindSpore-scientific/code-10/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/MindSpore-scientific/code-11/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/MindSpore-scientific/code-13/tree/main/token_learner
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MindSpore-scientific/code-7/tree/main/VideoMAE
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MindSpore-scientific/code-8/tree/main/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/MindSpore-scientific/code-9/tree/main/Zero-DCE
Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network
https://github.com/MohammadRezaQaderi/Video-Swin-Transformer
https://github.com/Mohanned-Elkholy/BigGan-model-inversion-with-image-transformations
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/NetraInamdar/Deep_Learning_Project
https://github.com/NikolasEnt/soccernet-calibration-sportlight
https://github.com/Niteeshaballa/fastai-practice
https://github.com/OpenGVLab/TimeSuite
TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning
https://github.com/P2333/Max-Mahalanobis-Training
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/PaParaZz1/TemporalShiftModule
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/model_zoo/recognition/videoswin.md
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/pp-tsm.md
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/timesformer.md
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/PhysGame/PhysGame
PhysGame: Uncovering Physical Commonsense Violations in Gameplay Videos
https://github.com/Puzer/stylegan-encoder
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/QUVA-Lab/timeception
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.