https://github.com/aimagelab/rmsnet_soccer
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
Papers with Code 아카이브
Video Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/akoepke/audio-retrieval-benchmark
QuerYD: A video dataset with high-quality text and audio narrations
https://github.com/albanie/collaborative-experts
The End-of-End-to-End: A Video Understanding Pentathlon Challenge (2020)
https://github.com/albert100121/MLVR-Pytorch
The Visual Centrifuge: Model-Free Layered Video Representations
https://github.com/albertmillan/adversarial-training-pytorch
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/alchemi5t/MNADrc
https://github.com/alexandrosstergiou/Squeeze-and-Recursion-Temporal-Gates
https://github.com/alterzero/DBPN-Pytorch
The 2018 PIRM Challenge on Perceptual Image Super-resolution
https://github.com/alterzero/RBPN-PyTorch
The 2018 PIRM Challenge on Perceptual Image Super-resolution
https://github.com/alterzero/STARnet
The 2018 PIRM Challenge on Perceptual Image Super-resolution
https://github.com/amazon-science/tubelet-transformer
https://github.com/amerch/CIFAR100-Training
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/andrewilyas/ens-adv-train-attack
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/anonymous-authors-iclr2022-481/leap
Learning Temporally Causal Latent Processes from General Temporal Data
https://github.com/anse3832/MUSIQ
https://github.com/antoine77340/iccv17learning
Learning from Video and Text via Large-Scale Discriminative Clustering
https://github.com/apple/corenet
https://github.com/araffin/srl-zoo
Time-Contrastive Networks: Self-Supervised Learning from Video
https://github.com/ariG23498/TokenLearner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/ariel415el/PerceptualLossGLO-Pytorch
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/arobey1/advbench
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/arobey1/mbrdl
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/arunos728/MotionSqueeze
MotionSqueeze: Neural Motion Feature Learning for Video Understanding
https://github.com/arunos728/arunos728.github.io
MotionSqueeze: Neural Motion Feature Learning for Video Understanding
https://github.com/ashutosh1919/FaceGenerationStyleGAN
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/atriumlts/subpixel
https://github.com/avisekiit/wacv_2019
https://github.com/ayushgupta9198/gan
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/ayushgupta9198/stylegan
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/baidut/PaQ-2-PiQ
From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
https://github.com/baidut/PatchVQ
https://github.com/bbednarski9/MaskTrackRCNN_DeepSort
https://github.com/bespontaneous/ffn
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/bethgelab/cifar10_challenge
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/bingcheng45/hnr-extension
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/boyellow/adaad
Towards Deep Learning Models Resistant to Adversarial Attacks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.