Papers with Code 아카이브
Object Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/carlosb1/upc-aidl-19-team4
https://github.com/cavedunissin/deeplearning_robot_jetson_nano
https://github.com/cges60809/DenseNet
https://github.com/charlesq34/3dcnn.torch
Volumetric and Multi-View CNNs for Object Classification on 3D Data
https://github.com/cheonlian/DeepfakeBlock
TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems
https://github.com/chinakook/hr101_mxnet
https://github.com/chingyaoc/DCL
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/chinokenochkan/3dgan-keras
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
https://github.com/chongweiliu/DUT-USEG
https://github.com/chrislauyc/Data-Science-Projects
https://github.com/chunliangli/Point-Cloud-GAN
https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations
https://github.com/cjrd/self-supervised-pretraining
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/clabrugere/pytorch-scarf
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/clip-italian/clip-italian
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/clxiao/Tiny_Faces_in_Tensorflow
https://github.com/cmasch/densenet
https://github.com/code-implementation1/Code1/tree/main/DeepID
https://github.com/code-implementation1/Code1/tree/main/densenet
https://github.com/code-implementation1/Code6/tree/main/ProtoNet
Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022
https://github.com/code-implementation1/Code7/tree/main/ResidualAttentionNet
https://github.com/code-implementation1/Code8/tree/main/simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/code-implementation1/Code9/tree/main/PVNet
PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition
https://github.com/codelion/adaptive-classifier
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning
https://github.com/coxlab/prednet
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
https://github.com/cs20m072/DL-assignment-2-Part-B
Improving neural networks by preventing co-adaptation of feature detectors
https://github.com/cszzshi/SimD
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/cydonia999/Tiny_Faces_in_Tensorflow
https://github.com/cyrilvallez/image-manipulation-detection
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/czero69/acomoeye-NN
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
https://github.com/danielbis/python_cheap_fashion
https://github.com/danielzgsilva/MOT
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/datoboat/Vehicle-Detection
https://github.com/daveboat/spp
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://github.com/davidczy/supcon_gamma
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.