https://github.com/jeffreyyihuang/two-stream-action-recognition
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
Papers with Code 아카이브
Video Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 7
- 평가표
- 0
- 보존된 평가 기록
- 0
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/jerrywn121/TianChi_AIEarth
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/jokeryan/post_training
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/jonnys1226/ego4d_asl
InternVideo-Ego4D: A Pack of Champion Solutions to Ego4D Challenges
https://github.com/jooyae/NVIDIA_STYLEGAN
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/jpeyre/unrel
Learning from Video and Text via Large-Scale Discriminative Clustering
https://github.com/kentsyx/Neural-IMage-Assessment
https://github.com/keras-team/keras-io/blob/master/examples/vision/token_learner.py
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/keunwoochoi/Auralisation
https://github.com/khieu/cifar10_challenge
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/khu-vll/cast
CAST: Cross-Attention in Space and Time for Video Action Recognition
https://github.com/khurram702/StyleBasedGAN
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/kingcong/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/kkahatapitiya/X3D-Multigrid
https://github.com/kozistr/gan-metrics
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/ktr-hubrt/WSAL
https://github.com/lemonadec/Relevance-between-Accuracy-under-Black-box-Attack-and-the-Similarity-between-Networks
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/lilygeorgescu/AED-SSMTL
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
https://github.com/linrongc/youtube-8m
https://github.com/liu824/representation-flow-for-action-recognition-paddlepaddle
https://github.com/lizatish/My_CNN
The 2018 PIRM Challenge on Perceptual Image Super-resolution
https://github.com/ljang0/videowebarena
VideoWebArena: Evaluating Long Context Multimodal Agents with Video Understanding Web Tasks
https://github.com/lntzm/cvpr24track-longvideo
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
https://github.com/locuslab/convex_adversarial
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/locuslab/robust_overfitting
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/loes5307/vocaladversary2022
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/louisYen/S3R
https://github.com/lucidrains/TimeSformer-pytorch
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/lucidrains/flamingo-pytorch
https://github.com/luizgh/adversarial_signatures
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/lunaproject22/aar
Spatial-Temporal Transformer for Dynamic Scene Graph Generation
https://github.com/lxtgh/omg-seg
OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding
https://github.com/m-3lab/awesome-visual-sensory-anomaly-detection
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
https://github.com/m-bain/video-transformers
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/matanbt/attack-tabular
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/mayu-ot/hidden-challenges-MR
Uncovering Hidden Challenges in Query-Based Video Moment Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.