https://github.com/chenhsing/svformer
공식 코드SVFormer: Semi-supervised Video Transformer for Action Recognition
Papers with Code 아카이브
Video Recognition
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Video Recognition is a process of obtaining, processing, and analysing data that it receives from a visual source, specifically video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/chuhanxx/helping_hand_for_egocentric_videos
공식 코드Helping Hands: An Object-Aware Ego-Centric Video Recognition Model
https://github.com/cvmi-lab/reskd
공식 코드Rethinking Resolution in the Context of Efficient Video Recognition
https://github.com/cybercore-co-ltd/VidConv
공식 코드VidConv: A modernized 2D ConvNet for Efficient Video Recognition
https://github.com/daniilidis-group/spherical-cnn
공식 코드https://github.com/daniilidis-group/swscnn
공식 코드https://github.com/deep-kaixun/videopure
공식 코드VideoPure: Diffusion-based Adversarial Purification for Video Recognition
https://github.com/deepsota/astfocus
공식 코드Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos
https://github.com/dingmyu/NCP
공식 코드Learning Versatile Neural Architectures by Propagating Network Codes
https://github.com/divyakraman/ECCV2022_FARFourierAerialVideoRecognition
공식 코드https://github.com/dragonlzm/pave
공식 코드https://github.com/dun-research/haltingvt
공식 코드HaltingVT: Adaptive Token Halting Transformer for Efficient Video Recognition
https://github.com/event-ahu/tscformer
공식 코드Unleashing the Power of CNN and Transformer for Balanced RGB-Event Video Recognition
https://github.com/facebookresearch/OctConv
공식 코드Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/facebookresearch/SlowFast
공식 코드https://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/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/farewellthree/stan
공식 코드Revisiting Temporal Modeling for CLIP-based Image-to-Video Knowledge Transferring
https://github.com/francis-rings/ila
공식 코드Implicit Temporal Modeling with Learnable Alignment for Video Recognition
https://github.com/ftkr12/rostfine
공식 코드Automated Sperm Assessment Framework and Neural Network Specialized for Sperm Video Recognition
https://github.com/google-research/scenic
공식 코드TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/hanbyel0105/CamDistHumanPose3D
공식 코드Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning
https://github.com/haoyanbin918/group-contextualization
공식 코드https://github.com/hayatkhan8660-maker/DVFL-Net
공식 코드https://github.com/hikvision-research/cost
공식 코드Collaborative Spatio-temporal Feature Learning for Video Action Recognition
https://github.com/holistic-video-understanding/HVU-Dataset
공식 코드https://github.com/ibm/sifar-pytorch
공식 코드https://github.com/iduta/iresnet
공식 코드https://github.com/iduta/pyconv
공식 코드Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
https://github.com/jinyjelly/videomamba
공식 코드https://github.com/joe-siyuan-qiao/WeightStandardization
공식 코드Micro-Batch Training with Batch-Channel Normalization and Weight Standardization
https://github.com/kay-ck/bsc-attack
공식 코드Attacking Video Recognition Models with Bullet-Screen Comments
https://github.com/kenshohara/3D-ResNets-PyTorch
공식 코드Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.