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/tengshaofeng/ResidualAttentionNetwork-pytorch
https://github.com/tensorflow/models
https://github.com/tensorflow/models/tree/master/official/vision/beta/projects/simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/tensorflow/models/tree/master/research/slim
https://github.com/thanhhvnqb/pydmobilenet_mxnet
PydMobileNet: Improved Version of MobileNets with Pyramid Depthwise Separable Convolution
https://github.com/the-super-toys/glimpse-models
Faster gaze prediction with dense networks and Fisher pruning
https://github.com/thefonseca/predictive-coding
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
https://github.com/thesfinox/ml-cicy
https://github.com/thunderInfy/simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/tianyu-tristan/Visual-Attention-Model
https://github.com/tiger154/captcha-solver-custom
https://github.com/tiskw/patchcore-ad
Billion-scale semi-supervised learning for image classification
https://github.com/titu1994/DenseNet
https://github.com/tmdgus1237/HelloWorld2018-2
https://github.com/tmlr-group/class_prior
https://github.com/tmlr-group/neglabel
https://github.com/tmoopenn/seq-nms
https://github.com/towhee-io/towhee
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/transcendent-ai-labs/dynaml
https://github.com/trsvchn/deep-textures
https://github.com/tuan-datascience/cifar10
https://github.com/tuannamnguyen93/DFKI_test_PhD
https://github.com/tuannamnguyen93/test_PhD
https://github.com/tuyentv2009/Face-Recognition-
https://github.com/tyler-hayes/embedded-cl
F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object Learning
https://github.com/ucas-vg/p2bnet
P2Object: Single Point Supervised Object Detection and Instance Segmentation
https://github.com/uclanlp/visualbert
Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions
https://github.com/umangkeshri/JBM-classifying-defected-parts
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/url-kaist/patchwork-plusplus
https://github.com/ushmabhatt/Contrastive-Learning
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/ut-vision/simhand
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/vadimkantorov/contextlocnet
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
https://github.com/varununleashed/tiny_faces_temp
https://github.com/venkai/RBDN
https://github.com/vfdev-5/BootstrappingLoss
Training Deep Neural Networks on Noisy Labels with Bootstrapping
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.