https://github.com/vicco-group/thingsvision
Harmonizing the object recognition strategies of deep neural networks with humans
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/vigansub/StructureAwareShapeTemplates
https://github.com/vijayvee/behavior-recognition
https://github.com/vijayvee/behavior_recognition
https://github.com/vikrant7/pytorch-looking-fast-and-slow
Looking Fast and Slow: Memory-Guided Mobile Video Object Detection
https://github.com/visionxlab/lrs-vqa
https://github.com/vlcekl/n2n-tomo
https://github.com/vlomonaco/ar1-pytorch
Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches
https://github.com/vlomonaco/core50
Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches
https://github.com/vohoaiviet/OverFeat-DeepNet
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
https://github.com/vpaliwal1/Image-Classification-using-CNN
https://github.com/vturrisi/solo-learn
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/w510056105/DeepLearning
Improving neural networks by preventing co-adaptation of feature detectors
https://github.com/wangbinglin1995/tianchi
https://github.com/wannabeOG/MAS-PyTorch
https://github.com/watay147/tele_project
Generate To Adapt: Aligning Domains using Generative Adversarial Networks
https://github.com/weiwang2330/siamese
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/wikke/Tianchi-Medical-LungTumorDetect
https://github.com/wilile26811249/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/willT97/Zero-shot-Image-Captioner
https://github.com/witcher425/CHINESEOCR
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
https://github.com/wvangansbeke/Unsupervised-Classification
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/xcmax/FewShotWithoutForgetting
https://github.com/xggIoU/GCNet_global_context_module_tensorflow
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
https://github.com/xiamenwcy/extended-caffe
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://github.com/xiangyu-liu/Computer-Network
Improving neural networks by preventing co-adaptation of feature detectors
https://github.com/xiaoboCASIA/SV-X-Softmax
https://github.com/xiaofengShi/CHINESE-OCR
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
https://github.com/xiaozhangyes/cognitioncapturer
https://github.com/xiuyu0000/new_papers_codes/tree/main/ResidualAttentionNet
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/backbones/googlenet.py
https://github.com/xternalz/SDPoint
https://github.com/xufanxiong/classification-of-caltech-256
https://github.com/xypan1232/iDeepE
https://github.com/y2l/meta-transfer-learning
https://github.com/y2l/meta-transfer-learning-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.