https://github.com/facebookresearch/TimeSformer
공식 코드Is Space-Time Attention All You Need for Video Understanding?
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/facebookresearch/VMZ
공식 코드Video Classification with Channel-Separated Convolutional Networks
https://github.com/facebookresearch/detectron2/tree/main/projects/MViTv2
공식 코드MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/facebookresearch/hiera
공식 코드Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
https://github.com/facebookresearch/lavila
공식 코드https://github.com/facebookresearch/maws
공식 코드The effectiveness of MAE pre-pretraining for billion-scale pretraining
https://github.com/facebookresearch/memvit
공식 코드MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition
https://github.com/facebookresearch/mvit
공식 코드MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/facebookresearch/omnivore
공식 코드https://github.com/facebookresearch/video-distant-supervision
공식 코드Learning To Recognize Procedural Activities with Distant Supervision
https://github.com/facebookresearch/video-long-term-feature-banks
공식 코드https://github.com/facebookresearch/video-nonlocal-net
공식 코드https://github.com/facebookresearch/vissl
공식 코드Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
https://github.com/facebookresearch/vissl/blob/main/MODEL_ZOO.md
공식 코드ClusterFit: Improving Generalization of Visual Representations
https://github.com/fandulu/asad
공식 코드Actor-identified Spatiotemporal Action Detection --- Detecting Who Is Doing What in Videos
https://github.com/feichtenhofer/twostreamfusion
공식 코드Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/fidsinn/sporttaskme22
공식 코드Fine-Grained Action Detection with RGB and Pose Information using Two Stream Convolutional Networks
https://github.com/francis-rings/ila
공식 코드Implicit Temporal Modeling with Learnable Alignment for Video Recognition
https://github.com/fuchenustc/sifa
공식 코드https://github.com/google-research/google-research/tree/master/vatt
공식 코드VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/google-research/scenic
공식 코드https://github.com/google-research/scenic/tree/main/scenic/projects/mbt
공식 코드https://github.com/google/graph_distillation
공식 코드Graph Distillation for Action Detection with Privileged Modalities
https://github.com/gorjanradevski/revisiting-spatial-temporal-layouts
공식 코드Revisiting spatio-temporal layouts for compositional action recognition
https://github.com/gsig/temporal-fields
공식 코드https://github.com/guoxih/Busy-Quiet-Video-Disentangling-for-Video-Classification
공식 코드https://github.com/guoxih/region-based-non-local-network
공식 코드https://github.com/hadjisma/VideoAlignment
공식 코드Representation Learning via Global Temporal Alignment and Cycle-Consistency
https://github.com/hikvision-research/skelact
공식 코드Skeleton-based Action Recognition with Convolutional Neural Networks
https://github.com/hitachi-rd-cv/CDB-loss
공식 코드Class-Difficulty Based Methods for Long-Tailed Visual Recognition
https://github.com/hitcbw/lpl
공식 코드Language-Assisted Human Part Motion Learning for Skeleton-Based Temporal Action Segmentation
https://github.com/holistic-video-understanding/HVU-Dataset
공식 코드https://github.com/ialhashim/DenseDepth
공식 코드High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/ideal-idea/sap
공식 코드https://github.com/iduta/pyconv
공식 코드Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
https://github.com/iriya99/ovre
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.