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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

6

평가표

1

보존된 평가 기록

1

코드

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

https://github.com/2024-MindSpore-1/Code6/tree/main/augvit

How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

https://github.com/2024-MindSpore-1/Code7/tree/main/CBAM

CBAM: Convolutional Block Attention Module

https://github.com/2024-MindSpore-1/Code7/tree/main/DBPN

Deep Back-Projection Networks for Single Image Super-resolution

https://github.com/2024-MindSpore-1/Code7/tree/main/centernet

CenterNet: Keypoint Triplets for Object Detection

https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_det

CenterNet: Keypoint Triplets for Object Detection

https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet101

Deep Residual Learning for Image Recognition

https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet50_v1

Deep Residual Learning for Image Recognition

https://github.com/2024-MindSpore-1/Code7/tree/main/cnn_direction_model

Structured Sparse R-CNN for Direct Scene Graph Generation

https://github.com/2024-MindSpore-1/Code7/tree/main/csd

Towards Compact Single Image Super-Resolution via Contrastive Self-distillation

https://github.com/2anchao/VovJpu

An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection

https://github.com/2gunsu/monocon-pytorch

Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection

https://github.com/2manslkh/korean-food-classification-flask

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/329tyson/fai-ssl-challenge

Scaling and Benchmarking Self-Supervised Visual Representation Learning

https://github.com/3P2S/arcface

Deep Residual Learning for Image Recognition

https://github.com/3dperceptionlab/therobotrix

UnrealROX: An eXtremely Photorealistic Virtual Reality Environment for Robotics Simulations and Synthetic Data Generation

https://github.com/3dperceptionlab/visual-wetlandbirds

MViTv2: Improved Multiscale Vision Transformers for Classification and Detection

https://github.com/3dtopia/dynamiccity

DynamicCity: Large-Scale 4D Occupancy Generation from Dynamic Scenes

https://github.com/3epochs/you-only-look-once

You Only Look Once: Unified, Real-Time Object Detection

https://github.com/44aayush/Denoising-and-Enhancement

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/497626895/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/4m4npr33t/SuperResolution-using-GANs

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://github.com/4uiiurz1/pytorch-deform-conv-v2

Deformable ConvNets v2: More Deformable, Better Results

https://github.com/4uiiurz1/pytorch-nested-unet

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

https://github.com/545641826/espcn

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

https://github.com/593903762/center

Objects as Points

https://github.com/593903762/ttf

Training-Time-Friendly Network for Real-Time Object Detection

https://github.com/5m0k3/gwd-efficientdet-pytorch

Dynamic Scale Training for Object Detection

https://github.com/5m0k3/gwd-yolov5-pytorch

Dynamic Scale Training for Object Detection

https://github.com/6-dl/darknet_wpb

YOLOv3: An Incremental Improvement

https://github.com/6210612757/facerecognition

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/6QHTSK/paper-database

Mind the Pad -- CNNs can Develop Blind Spots

https://github.com/812618101/TAL-Demo

Gaussian Temporal Awareness Networks for Action Localization

https://github.com/965694547/hybrid-system-of-frame-wise-model-and-sedt

Sound Event Detection Transformer: An Event-based End-to-End Model for Sound Event Detection

https://github.com/96jonesa/StyleGan2-Colab-Demo

Efficient Attention: Attention with Linear Complexities

https://github.com/979277/2023TCSVT_TEAN

Scene Text Image Super-Resolution in the Wild

https://github.com/97andrea97/CenterNet-PNPDet

Objects as Points

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