https://github.com/prasadsawant5/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/pren1/Group-Normalization-Example
https://github.com/princeton-nlp/copycat
https://github.com/princeton-vl/SimpleView
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/princeton-vl/d3dhelper
https://github.com/prinshul/GWSDR
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/priyavrat-misra/xrays-and-gradcam
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/projectdossier/medvid2022
A Dataset for Medical Instructional Video Classification and Question Answering
https://github.com/psychopa4/PFNL
https://github.com/pvtien96/D2DP
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/pvtien96/Detectron2DeepSortPlus
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/pwc-1/Paper-10/tree/main/seggpt
https://github.com/pwc-1/Paper-10/tree/main/swin
https://github.com/pwc-1/Paper-10/tree/main/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/pwc-1/Paper-5/tree/main/megatron_gpt2
GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization
https://github.com/pwc-1/Paper-9/tree/main/1/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/pwc-1/Paper-9/tree/main/1/vivit
https://github.com/pwc-1/Paper-9/tree/main/2/megatron_gpt2
GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization
https://github.com/pwc-1/Paper-9/tree/main/2/minigpt4
https://github.com/pwc-1/Paper-9/tree/main/3/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/pwc-1/Paper-9/tree/main/3/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/pwc-1/Paper-9/tree/main/4/ACGNet
ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization
https://github.com/pwc-1/Paper-9/tree/main/4/pixelshuffle1d
https://github.com/pwc-1/Paper-9/tree/main/4/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/pwc-1/Paper-9/tree/main/5/videomae
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/pwc-1/Paper-9/tree/main/5/x_clip
ICSVR: Investigating Compositional and Syntactic Understanding in Video Retrieval Models
https://github.com/pwc-1/Paper-9/tree/main/7/Translation-Invariant/model
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
https://github.com/pwc-1/Paper-9/tree/main/7/pixelshuffle1d
https://github.com/pwc-1/Paper-9/tree/main/7/token_learner
TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
https://github.com/pytorch/captum
https://github.com/pytorch/vision
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/qema/orca-py
https://github.com/qijiezhao/pseudo-3d-pytorch
Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
https://github.com/qingswu/deepmot_mirrored
https://github.com/qingzhiwu/Temporal-Context-Aggregation-Network-Pytorch
Temporal Context Aggregation Network for Temporal Action Proposal Refinement
https://github.com/qingzhiwu/pytorch-act-detector
Action Tubelet Detector for Spatio-Temporal Action Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.