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

https://github.com/THHHomas/mls

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

https://github.com/TIGER-AI-Lab/ImagenHub

GenAI Arena: An Open Evaluation Platform for Generative Models

https://github.com/TRAILab/ST-SLidR

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/TaatiTeam/STARS

STARS: Self-supervised Tuning for 3D Action Recognition in Skeleton Sequences

https://github.com/TaatiTeam/stgcn_parkinsonism_prediction

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/Taeuk-Jang/pointcompletion

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

https://github.com/TahmasbiM/Example

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

https://github.com/TanakitInt/SRCNN-anime

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/Tangshitao/ClipShots_basline

Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks

https://github.com/Taoudi/LotteryTicketHypothesis

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/TasneemMohammed/Engagement-Detection-Using-Hybrid-EfficientNetB7-Together-With-TCN-LSTM-and-Bi-LSTM

https://github.com/TejInaco/multimodalML

Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text

https://github.com/Tencent/ActionDetection-DBG

BSN: Boundary Sensitive Network for Temporal Action Proposal Generation

https://github.com/Tenfleques/mot

Simple Online and Realtime Tracking

https://github.com/TerenceLiu4444/Kaggle-solutions

Constrained-size Tensorflow Models for YouTube-8M Video Understanding Challenge

https://github.com/Tetsuya-Nishikawa/ConvLSTM_DEMO

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

https://github.com/The-AI-Summer/self-attention-cv

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

https://github.com/ThomasDupiereux/ADLproject

BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN

https://github.com/ThomasDupiereux/EnGAN

BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN

https://github.com/ThomasDupiereux/hpgan

HP-GAN: Probabilistic 3D human motion prediction via GAN

https://github.com/ThomasEhling/Weight_Standardization

Micro-Batch Training with Batch-Channel Normalization and Weight Standardization

https://github.com/Ti-Oluwanimi/Violence_Detection

SSIVD-Net: A Novel Salient Super Image Classification & Detection Technique for Weaponized Violence

https://github.com/Tianhao-Qi/DEADiff_code

StyleCrafter: Enhancing Stylized Text-to-Video Generation with Style Adapter

https://github.com/TobiasLee/Text-Classification

Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN

https://github.com/TomokiKomiya/SRGAN-keras

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

https://github.com/TonyYaoMSU/ProjectedGradientDescent

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/Tonyhuiii/color-transform

A Style-Aware Content Loss for Real-time HD Style Transfer

https://github.com/TooTouch/WhiteBox-Part1

Visualizing and Understanding Convolutional Networks

https://github.com/Tools4Project/4501Project

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/TraffiX-VideoQA/TUMTraffic-VideoQA-Baseline

TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes

https://github.com/TranThienDat-Nguyen/VisionEvaluation

Video Instance Segmentation

https://github.com/TripleJoy/SAM2MOT

SAM 2: Segment Anything in Images and Videos

https://github.com/TrustAI/DeepConcolic

Coverage Guided Testing for Recurrent Neural Networks

https://github.com/Trustworthy-AI-Group/TransferAttack

Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping

https://github.com/TsinghuaC3I/Efficient-Diffusion-Models

Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices

https://github.com/Tudouu/stgcn_light_op

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.