https://github.com/roydenwa/cell-centroid-former
A Simple Framework for Contrastive Learning of Visual Representations
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/ryanwebster90/image-synthesis-lab2
https://github.com/ryanwebster90/texture-synthesis-lab
https://github.com/ryersonvisionlab/two-stream-dyntex-synth
https://github.com/s-a-malik/multi-few
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/sa4s-serc/ecomls
https://github.com/sabaridsn/LightWeightModel
Light Weight Residual Dense Attention Net for Spectral Reconstruction from RGB Images
https://github.com/saigerutherford/anatomically_defined_CNNs
A Domain Guided CNN Architecture for Predicting Age from Structural Brain Images
https://github.com/sajjjadayobi/CLIPfa
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/salarim/Semi-Supervised-CL
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/salesforce/ensemble-of-averages
Billion-scale semi-supervised learning for image classification
https://github.com/salesforce/pb-ovd
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/sangHa0411/ImageNet
https://github.com/sanjaykrishnanrs/Project-7
https://github.com/sar-gupta/convisualize_nb
https://github.com/satishjasthi/Densenet_smplified
https://github.com/saurabh-kataria/projected-distribution-loss
https://github.com/sauravmishra1710/Inception---Going-Deeper-with-Convolutions/tree/main/InceptionV1
https://github.com/sayakpaul/SimCLR-in-TensorFlow-2
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/sbcblab/Keras-CoDeepNEAT
https://github.com/sberbank-ai/ru-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/se111/side
ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation
https://github.com/se4u/cylsh
https://github.com/seasonyc/densenet
https://github.com/serengil/deepface
https://github.com/sermanet/OverFeat
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
https://github.com/serre-lab/harmonization
https://github.com/serre-lab/meta-predictor
Harmonizing the object recognition strategies of deep neural networks with humans
https://github.com/serre-lab/prj_selfsup
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/sgrvinod/a-pytorch-tutorial-to-object-detection
https://github.com/sh21kang/OCR_V1
Handwritten Bangla Character Recognition Using The State-of-Art Deep Convolutional Neural Networks
https://github.com/shahidul56/Tiny_Faces_in_Tensorflow_msc
https://github.com/sheqi/Continual_Learning_CV
OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning
https://github.com/shivammehta25/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/shivani-raul/Image-Captioning-VGG16
https://github.com/shkarupa-alex/tfclip
Learning Transferable Visual Models From Natural Language Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.