https://github.com/deeplearningforfun/OctConv
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
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/deepmind/kinetics-i3d
https://github.com/demianzhang/weakly-action-localization
Weakly Supervised Action Localization by Sparse Temporal Pooling Network
https://github.com/dlpbc/keras-kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/dorsadadjoo/Distance_Estimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/dreamh1gh/awesome-srl
https://github.com/drv-agwl/ViViT-pytorch
https://github.com/dsshim0125/grmc
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/edgynieum/ashus_projects
https://github.com/epic-kitchens/epic-kitchens-55-action-models
https://github.com/facebookresearch/assemblyhands-toolkit
AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation
https://github.com/facebookresearch/detectron
https://github.com/facebookresearch/hiervl
https://github.com/facebookresearch/multimodal
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/facebookresearch/pytorchvideo
https://github.com/facebookresearch/xformers
Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers
https://github.com/farazahmeds/Classification-of-brain-tumor-using-Spatiotemporal-models
ConvNet Architecture Search for Spatiotemporal Feature Learning
https://github.com/fmthoker/severe-benchmark
https://github.com/franknb/Self-attention-DCGAN
https://github.com/frostinassiky/bsp
Boundary-sensitive Pre-training for Temporal Localization in Videos
https://github.com/gan3sh500/octaveconv-pytorch
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/google-deepmind/kinetics-i3d
https://github.com/google-research/google-research/tree/master/assemblenet
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
https://github.com/gsig/charades-algorithms
https://github.com/gudaykiran/DepthModel
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/guide2157/ChulaXrayClassifier
What Makes Training Multi-Modal Classification Networks Hard?
https://github.com/gulvarol/bsl1k
BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues
https://github.com/gulvarol/surreact
Synthetic Humans for Action Recognition from Unseen Viewpoints
https://github.com/guoxih/busy-quiet-net
https://github.com/halixness/generative_timesformer_pytorch
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/hangzhaomit/HACS-dataset
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization
https://github.com/haofanwang/video-swin-transformer-pytorch
https://github.com/hassony2/kinetics_i3d_pytorch
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/helloxy96/CS5242_Project2020
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/hikvision-research/cost
https://github.com/hongyuanyu/span
VideoMamba: State Space Model for Efficient Video Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.