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

https://github.com/h4nwei/SPAQ

https://github.com/haaz77/keras-tutorials

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/halixness/generative_timesformer_pytorch

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

https://github.com/hampen2929/CenterTrack

Tracking Objects as Points

https://github.com/handasontam/GAT-with-edgewise-attention

Graph Attention Networks

https://github.com/hangzhaomit/HACS-dataset

HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization

https://github.com/hao-ai-lab/fastvideo

Fast Video Generation with Sliding Tile Attention

https://github.com/haochenheheda/lvvis

Occluded Video Instance Segmentation: A Benchmark

https://github.com/haofanwang/video-swin-transformer-pytorch

Video Swin Transformer

https://github.com/happen2me/cross-gnn

Flamingo: a Visual Language Model for Few-Shot Learning

https://github.com/happywu/Sequence-Level-Semantics-Aggregation

Sequence Level Semantics Aggregation for Video Object Detection

https://github.com/hariharannatesh/Action-Recognition-using-Pytorch-Geometric

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/harlanhong/MM2021-CO2-Net

Cross-modal Consensus Network for Weakly Supervised Temporal Action Localization

https://github.com/harsh2912/people-tracking

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/harunshimanto/Neural-Style-Transfer-of-Artistic-Style

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/hasibzunair/peekaboo

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/hassony2/kinetics_i3d_pytorch

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

https://github.com/hazdzz/ChebyNet

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

https://github.com/hazdzz/GCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/hazdzz/SGC

Simplifying Graph Convolutional Networks

https://github.com/hbzhang/cvpr2020

Learning a Text-Video Embedding from Incomplete and Heterogeneous Data

https://github.com/hcygeorge/aoi_defect_detection

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/heartyguy/AI-AngryBird-Eagle-Wing

XGBoost: A Scalable Tree Boosting System

https://github.com/hehefan/P4Transformer

https://github.com/hehefan/PSTNet2

https://github.com/helloxy96/CS5242_Project2020

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

https://github.com/henriquemeloo/cat-dog-image-classifier

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/henry8527/GCE

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/henrytsui000/YOLO

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

https://github.com/hhe-distance/AIF-CNN

Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation

https://github.com/hi-archers/stableke

MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions

https://github.com/hidekifujinami6/gan_evaluation

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

https://github.com/hieu2906090/deep-learning-in-js

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/highwaywu/imageforensicsosn

https://github.com/hildekuehne/Weak_YouTube_dataset

Mining YouTube - A dataset for learning fine-grained action concepts from webly supervised video data

https://github.com/hitsz-tmg/videovista

VideoVista: A Versatile Benchmark for Video Understanding and Reasoning

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