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/debanjanxy/GNR-652
https://github.com/deboleenaroy/TreeCNN
Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental Learning
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https://github.com/deeplearning-wisc/cider
https://github.com/deeplearning-wisc/dice
https://github.com/deeplearning-wisc/gradnorm_ood
https://github.com/deeplearning-wisc/knn-ood
https://github.com/deeplearning-wisc/large_scale_ood
https://github.com/deeplearning-wisc/npos
https://github.com/deeplearning-wisc/react
https://github.com/deeplearning-wisc/snn
https://github.com/deepmind/kinetics-i3d
https://github.com/delyan-boychev/grad-cache-con-learning
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/deontaepharr/Residual-Attention-Network
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A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/dhansmair/flamingo-mini
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/diksha0799/Image-Captioning-master
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HAKE: A Knowledge Engine Foundation for Human Activity Understanding
https://github.com/djcustard/GestureGAN
https://github.com/dnouri/cuda-convnet
Improving neural networks by preventing co-adaptation of feature detectors
https://github.com/dongyangli-del/eeg_image_decode
https://github.com/dr-darryl-wright/Noisy-Labels-with-Bootstrapping
Training Deep Neural Networks on Noisy Labels with Bootstrapping
https://github.com/dreamercv/HSResNet-MMAL
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
https://github.com/dslisleedh/EnsNet-tensorflow2
Ensemble learning in CNN augmented with fully connected subnetworks
https://github.com/dtuia/KEMA
https://github.com/eaguaida/TF_EnsNet-
Ensemble learning in CNN augmented with fully connected subnetworks
https://github.com/earthLD/Algorithm
https://github.com/eavnjeong/ief-vad
Expanding Event Modality Applications through a Robust CLIP-Based Encoder
https://github.com/eclique/keras-gradcam
https://github.com/edreismd/convirt-pytorch
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/edureisMD/ConVIRT-pytorch
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/eghouti/SAL
https://github.com/eify/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/emanuelevivoli/CoMix
Comics Datasets Framework: Mix of Comics datasets for detection benchmarking
https://github.com/eminorhan/baby-vision
A Simple Framework for Contrastive Learning of Visual Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.