Papers with Code 아카이브
Object Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 9
- 보존된 평가 기록
- 31
Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.
<span style="color:grey; opacity: 0.6">( Image credit: Tensorflow Object Detection API )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Jeremyyny/Value-Spectrum
https://github.com/JoegameZhou/mPanGu-Alpha-53
PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
https://github.com/JonasWechsler/DeepLearningLab5
https://github.com/JoonHyung-Park/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/JunweiLiang/Object_Detection_Tracking
https://github.com/KDL-umass/saliency_maps
Transparency and Explanation in Deep Reinforcement Learning Neural Networks
https://github.com/KU-CVLAB/MoA
https://github.com/Kadenze/siamese_net
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/Kashi7/Object-Recognition
https://github.com/Kaushalya/medclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Kodiologist/Citematic
https://github.com/LJKS/noplants
https://github.com/LONG-9621/3DCNN
Volumetric and Multi-View CNNs for Object Classification on 3D Data
https://github.com/LanaSina/chainer_prednet
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
https://github.com/Ldpe2G/PCANet
PCANet: A Simple Deep Learning Baseline for Image Classification?
https://github.com/LiYingwei/ShapeTextureDebiasedTraining
https://github.com/LinearPi/OCR_Chinese
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
https://github.com/LisaAnne/DCC
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data
https://github.com/Liut2016/ecg-supcontrast
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/Liuyubao/transfer-learning
https://github.com/LuanAdemi/VisualGo
Chessboard and chess piece recognition with the support of neural networks
https://github.com/ML-Bee/associative-alignment-fs
https://github.com/MRLoghmani/relative-rotation
Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/googlenet
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/vgg
https://github.com/MartinBuessemeyer/Artistic-Texture-Control
https://github.com/MartinThoma/clana
Analysis and Optimization of Convolutional Neural Network Architectures
https://github.com/Mayurji/Image-Classification-PyTorch
https://github.com/Mayurji/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/Mikkil5112/PredNet
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
https://github.com/Mind23-2/MindCode-169
https://github.com/Mind23-2/MindCode-76
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/Mind23-2/MindCode-88/tree/main/SPPNet
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://github.com/MindCode-4/code-7/tree/main/factorized-attention
https://github.com/MindCode-4/code-8/tree/main/Projected-Distribution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.