https://github.com/Ruby1302/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
Papers with Code 아카이브
Action Classification
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Image source: The Kinetics Human Action Video Dataset
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Ruby1302/DenseDepth_1
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/STONE-SAMA/x3d_mindspore
X3D: Expanding Architectures for Efficient Video Recognition
https://github.com/SforAiDl/vformer
https://github.com/SharadGitHub/OctaveUnet
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/ShobhitMaheshwari/sign-language1
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Silver-L/OctConv
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/SilvioGiancola/SoccerNet-code
SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
https://github.com/StanfordVL/RubiksNet
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Sushmitha-Katti/DepthModel
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/The-AI-Summer/self-attention-cv
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/WavesUR/embedded_TSM
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/WeLoveKiraboshi/In-PlaneRotation-AwareDepthEstimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/WeLoveKiraboshi/In-PlaneRotationAwareDepthEstimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/Westlake-AI/openmixup
Masked Feature Prediction for Self-Supervised Visual Pre-Training
https://github.com/Whiffe/mmaction2_YF
https://github.com/X-PLUG/mPLUG-2
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
https://github.com/XuHao9166/TSN_BehaviorRecognition_ForwardCalculation
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/XuHao9166/TSN_BehaviorRecognition_GenerateTrainImages
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/Ysp9714/SlowFastNet-keras
https://github.com/ZJCV/Non-local
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/ZJCV/TSN
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/ZJCV/X3D
X3D: Expanding Architectures for Efficient Video Recognition
https://github.com/ZJUT-ERCISS/zjut_mindvideo/blob/master/msvideo/models/i3d.py
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/ZJUT-ERCISS/zjut_mindvideo/blob/master/msvideo/models/r2plus1d.py
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/Ziyang412/VideoTree
https://github.com/acewjh/Video-Swin-Transformer
https://github.com/ahsaniqbal/Kinetics-FeatureExtractor
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/aim3-ruc/youmakeup_challenge2022
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/aimbsg/EVA4_S15
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/akashe/ProgrammingInterview
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/alinstein/Depth_estimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/amazon-science/tubelet-transformer
https://github.com/amitakamath/hard_positives
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/amitakamath/whatsup_vlms
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/ammesatyajit/VideoBERT
VideoBERT: A Joint Model for Video and Language Representation Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.