https://github.com/openai/cleverhans
Towards Deep Learning Models Resistant to Adversarial Attacks
Papers with Code 아카이브
Video Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/opengvlab/videochat-flash
VideoEval-Pro: Robust and Realistic Long Video Understanding Evaluation
https://github.com/opengvlab/videochat-r1
VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning
https://github.com/pavancm/vbliinds
https://github.com/pbaylies/stylegan-encoder
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/peck94/cann-detector
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/photosynthesis-team/piq
Mean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluator
https://github.com/pikinder/nn-patterns
https://github.com/pku-yuangroup/video-bench
https://github.com/pomonam/AttentionCluster
Learnable pooling with Context Gating for video classification
https://github.com/projectdossier/medvid2022
A Dataset for Medical Instructional Video Classification and Question Answering
https://github.com/pwc-1/Paper-10/tree/main/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/pwc-1/Paper-9/tree/main/1/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/pwc-1/Paper-9/tree/main/2/minigpt4
https://github.com/pwc-1/Paper-9/tree/main/3/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/pwc-1/Paper-9/tree/main/4/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/pwc-1/Paper-9/tree/main/5/videomae
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/pwc-1/Paper-9/tree/main/5/x_clip
ICSVR: Investigating Compositional and Syntactic Understanding in Video Retrieval Models
https://github.com/pwc-1/Paper-9/tree/main/7/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/pytorch/captum
https://github.com/qingzhiwu/Temporal-Context-Aggregation-Network-Pytorch
Temporal Context Aggregation Network for Temporal Action Proposal Refinement
https://github.com/qiujihao19/artemis
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
https://github.com/qjy981010/CMaskTrack-RCNN
https://github.com/r-khanna/Visualizing-and-Understanding-Convolutional-Networks
https://github.com/rainyBJ/RTD_RePro
Relaxed Transformer Decoders for Direct Action Proposal Generation
https://github.com/revbucket/mister_ed
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/rijuldhir/TSM
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/rish-16/tokenlearner-pytorch
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/rohitgirdhar/CATER
CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning
https://github.com/roudimit/AVLnet
CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval
https://github.com/ryanxingql/image-quality-assessment-toolbox
Learning a No-Reference Quality Metric for Single-Image Super-Resolution
https://github.com/salmank255/ROADSlowFast
Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization
https://github.com/salomonhotegni/MOREL
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/sar-gupta/convisualize_nb
https://github.com/scenarri/s2m-tea
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/sdkchris/Exercise-Sheet-1
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.