https://github.com/2023-MindSpore-4/Code11/tree/main/SRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/2023-MindSpore-4/Code11/tree/main/slowfast
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/gpt2
GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/swin
https://github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_12_computational-performance
https://github.com/2023-MindSpore-4/Code5/tree/main/D2Co
https://github.com/2023-MindSpore-4/Code6/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/2023-MindSpore-4/Code7/tree/main/SRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/2023-MindSpore-4/Code7/tree/main/slowfast
https://github.com/2023-MindSpore-4/Code7/tree/main/srcnn
https://github.com/2023-MindSpore-4/Code7/tree/main/stnet
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
https://github.com/2023-MindSpore-4/Code8/tree/main/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/2023-MindSpore-4/Code8/tree/main/BMN
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/2023-MindSpore-4/Code8/tree/main/C3D
R-C3D: Region Convolutional 3D Network for Temporal Activity Detection
https://github.com/2023-MindSpore-4/Code9/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2023-MindSpore-4/Code9/tree/main/VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/2024-MindSpore-1/Code4/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/2024-MindSpore-1/Code6/tree/main/ADNet
https://github.com/2024-MindSpore-1/Code6/tree/main/AttentionCluster
Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification
https://github.com/2024-MindSpore-1/Code6/tree/main/BMN
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/2024-MindSpore-1/Code9/tree/main/LOGO--Mindspore-master
FLAG3D: A 3D Fitness Activity Dataset with Language Instruction
https://github.com/30Aniket/Graph-Convolutional-Networks-GCNs
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/360CVGroup/FancyVideo
FancyVideo: Towards Dynamic and Consistent Video Generation via Cross-frame Textual Guidance
https://github.com/360jinrong/GBST
https://github.com/3D-ResNets-Reproduction/Reference-Project
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/3ammor/Video-Pixel-Networks
https://github.com/404nofound/gat
https://github.com/4kubo/bacf_python
Learning Background-Aware Correlation Filters for Visual Tracking
https://github.com/4pygmalion/GAT_tensorflow
https://github.com/545641826/espcn
https://github.com/590shun/summarizer
https://github.com/590shun/video_summarize_dsf
https://github.com/590shun/vsum_dsf
https://github.com/812618101/TAL-Demo
Deep Concept-wise Temporal Convolutional Networks for Action Localization
https://github.com/89viper/Python-Deep_Video_Inpainting
https://github.com/9p15p/frtm-vos
Learning Fast and Robust Target Models for Video Object Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.