https://github.com/ZhihaoZhu/PointNet-Implementation-Tensorflow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Papers with Code 아카이브
Activity Recognition
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Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.
Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Ziyang412/VideoTree
https://github.com/a736875071/clip-vit-large-patch14
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/aakashrkaku/seq2seq_hrar
Sequence-to-Sequence Modeling for Action Identification at High Temporal Resolution
https://github.com/abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/abhilash1910/GraphAttentionNetworks
https://github.com/abhuse/pytorch-efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/abi-kothapalli/minimumdominatingsets
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/abramjos/Scene-boundary-detection
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
https://github.com/acewjh/Video-Swin-Transformer
https://github.com/adldotori/CGAN
https://github.com/agethen/RPAN
https://github.com/ahmed-anas/thesis-pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ahsaniqbal/Kinetics-FeatureExtractor
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/ai-forever/ru-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/aidarikako/JMDA
https://github.com/aim3-ruc/youmakeup_challenge2022
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/aimerykong/predictive-filter-flow
Multigrid Predictive Filter Flow for Unsupervised Learning on Videos
https://github.com/aj9011/Car-Speed-Prediction
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/ajayjain/vectorascent
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ajbisberg/gcn
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
https://github.com/ajemerson/CSC_722_Project
https://github.com/ajemerson/Recipe-Recommendation-System
https://github.com/ajertec/PointNetKeras
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ajhamdi/AdvPC
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ak24watch/Graph-Deeplearning-GAT
https://github.com/akash18tripathi/MAGNET-Multi-Label-Text-Classi-cation-using-Attention-based-Graph-Neural-Network
https://github.com/akashe/ProgrammingInterview
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/akshyta/Human-Activity-Recognition
The "something something" video database for learning and evaluating visual common sense
https://github.com/akuramshin/cDCGAN-Quickdraw
https://github.com/alex-f1tor/Image-Caption
https://github.com/alexOarga/haiku-geometric
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/ali-stanford/pointnetcfd
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/alibaba/EasyNLP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/alililia/ms_contrib/tree/main/ascend_gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/alililia/ms_contrib/tree/main/gpu_gcn
Semi-Supervised Classification with Graph Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.