https://github.com/facebookresearch/OctConv
공식 코드Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
Papers with Code 아카이브
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/facebookresearch/R2Plus1D
공식 코드A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/facebookresearch/SlowFast
공식 코드https://github.com/facebookresearch/TimeSformer
공식 코드Is Space-Time Attention All You Need for Video Understanding?
https://github.com/facebookresearch/VMZ
공식 코드Video Classification with Channel-Separated Convolutional Networks
https://github.com/facebookresearch/co-tracker
공식 코드https://github.com/facebookresearch/detectron2/tree/main/projects/MViTv2
공식 코드MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/facebookresearch/dino
공식 코드https://github.com/facebookresearch/grounded-video-description
공식 코드https://github.com/facebookresearch/hiera
공식 코드Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
https://github.com/facebookresearch/jepa-intuitive-physics
공식 코드Intuitive physics understanding emerges from self-supervised pretraining on natural videos
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/moviegenbench
공식 코드https://github.com/facebookresearch/mvit
공식 코드MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/facebookresearch/omnivore
공식 코드https://github.com/facebookresearch/perception_models
공식 코드PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding
https://github.com/facebookresearch/pytorchvideo
공식 코드PyTorchVideo: A Deep Learning Library for Video Understanding
https://github.com/facebookresearch/sam2
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/videoseal
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/facebookresearch/vizseq
공식 코드https://github.com/facebookresearch/vsc2022
공식 코드https://github.com/fan-aaron-zhang/bvi-aom
공식 코드BVI-AOM: A New Training Dataset for Deep Video Compression Optimization
https://github.com/fan-aaron-zhang/bvi-cr
공식 코드BVI-CR: A Multi-View Human Dataset for Volumetric Video Compression
https://github.com/fan-aaron-zhang/mf-mfrnet
공식 코드Enhancing HDR Video Compression through CNN-based Effective Bit Depth Adaptation
https://github.com/fandulu/DD-Net
공식 코드Make Skeleton-based Action Recognition Model Smaller, Faster and Better
https://github.com/fandulu/asad
공식 코드Actor-identified Spatiotemporal Action Detection --- Detecting Who Is Doing What in Videos
https://github.com/fangkaipeng/pros
공식 코드ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrieval
https://github.com/fanglaosi/skeleton-in-context
공식 코드Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning
https://github.com/fansunqi/akeys
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.