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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Oishani/Machine-Learning-Projects

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/OlineRanum/SAPA

Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison

https://github.com/OliverRensu/RTNet

https://github.com/One-sixth/pixelshuffle_invert_pytorch

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/Ontheway361/BSN_pytorch

BSN: Boundary Sensitive Network for Temporal Action Proposal Generation

https://github.com/OpenGVLab/TimeSuite

TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning

https://github.com/OpenXAIProject/LRP_for_LSTM_Korean_dataset

Explaining NonLinear Classification Decisions with Deep Taylor Decomposition

https://github.com/P2333/Max-Mahalanobis-Training

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/PKU-YuanGroup/LLMBind

LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment

https://github.com/PKU-YuanGroup/MoE-LLaVA

LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment

https://github.com/PKU-YuanGroup/UniWorld-V1

Open-Sora Plan: Open-Source Large Video Generation Model

https://github.com/PKU-YuanGroup/Video-LLaVA

LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment

https://github.com/PPPrior/i3d-pytorch

Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

https://github.com/PaParaZz1/PointNet

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/PaParaZz1/TemporalShiftModule

TSM: Temporal Shift Module for Efficient Video Understanding

https://github.com/PaddlePaddle/PASSL

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/PaddlePaddle/PaddleClas

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/mot

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

https://github.com/PaddlePaddle/PaddleMIX/tree/develop/paddlemix/examples

EVA: Exploring the Limits of Masked Visual Representation Learning at Scale

https://github.com/PaddlePaddle/PaddleScience

TGCN: Time Domain Graph Convolutional Network for Multiple Objects Tracking

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/applications/T2VLAD/README_en.md

T2VLAD: Global-Local Sequence Alignment for Text-Video Retrieval

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/model_zoo/recognition/videoswin.md

Video Swin Transformer

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/model_zoo/segmentation/cfbi.md

Collaborative Video Object Segmentation by Foreground-Background Integration

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/localization/bmn.md

BMN: Boundary-Matching Network for Temporal Action Proposal Generation

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/pp-tsm.md

TSM: Temporal Shift Module for Efficient Video Understanding

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/pp-tsn.md

Temporal Segment Networks for Action Recognition in Videos

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/slowfast.md

SlowFast Networks for Video Recognition

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/stgcn.md

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/timesformer.md

Is Space-Time Attention All You Need for Video Understanding?

https://github.com/PaddlePaddle/PaddleYOLO

PP-YOLOE: An evolved version of YOLO

https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/video/models/bsn

BSN: Boundary Sensitive Network for Temporal Action Proposal Generation

https://github.com/PaddlePaddle/models/tree/develop/dygraph/bmn

BMN: Boundary-Matching Network for Temporal Action Proposal Generation

https://github.com/ParitoshParmar/C3D-LSTM--PyTorch

Learning To Score Olympic Events

https://github.com/ParitoshParmar/HalluciNet--PyTorch

HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN

https://github.com/ParitoshParmar/LTSOE

Learning To Score Olympic Events

https://github.com/PaulEmmanuelSotir/DeepCV

Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition

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