https://github.com/nsom/Basic-Models
Very Deep Convolutional Networks for Large-Scale Image Recognition
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nsom/VGG16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nsubbaian/FrequentistML
https://github.com/nvlabs/eagle
Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models
https://github.com/nvlabs/long-video-gan
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/nvlong21/person_Counting
https://github.com/nyuolab/VIPER-torch
Video Prediction Models as Rewards for Reinforcement Learning
https://github.com/nzl-thu/musdl
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/oaqoe-DWQ/Yolov5_DeepSort_Pytorch
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/obvious-research/OnlyFlow
OnlyFlow: Optical Flow based Motion Conditioning for Video Diffusion Models
https://github.com/oellop/facial_expression
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ofrikleinfeld/ObjectPermanence
https://github.com/ogatarina-sq/secyolov7
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/ojedaf/TNT
TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video Classification
https://github.com/okeer/nst-tele-bot
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/oladeha2/shot_boudary_detector
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
https://github.com/olaviinha/NeuralImageSuperResolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/olivesgatech/3DPSD-VQA
Power of Tempospatially Unified Spectral Density for Perceptual Video Quality Assessment
https://github.com/olivesgatech/RoSE
Counterfactual Gradients-based Quantification of Prediction Trust in Neural Networks
https://github.com/olivesgatech/UNIQUE-Project-Repository
https://github.com/olivesgatech/UNIQUE-Unsupervised-Image-Quality-Estimation
https://github.com/oljikeboost/PlayerTracking
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
https://github.com/om-ai-lab/ZoomEye
OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer
https://github.com/omglu93/energy_consumption_eda_prediction
https://github.com/omkar13/MaskTrack
https://github.com/omkarghugarkar007/Neural_Super_Sampling
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/oncescuandreea/audio-retrieval
https://github.com/onethousand1000/eg3d-projector
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/opeco17/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/open-mmlab/mmaction2
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/open-mmlab/mmclassification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/open-mmlab/mmdetection
https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/basicvsr/README.md
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
https://github.com/open-mmlab/mmflow
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/open-mmlab/mmpose
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/open-mmlab/mmselfsup
Masked Feature Prediction for Self-Supervised Visual Pre-Training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.