https://github.com/waterdisappear/nudt4mstar
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
Depth Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 10
- 평가표
- 14
- 보존된 평가 기록
- 58
Depth Estimation is the task of measuring the distance of each pixel relative to the camera. Depth is extracted from either monocular (single) or stereo (multiple views of a scene) images. Traditional methods use multi-view geometry to find the relationship between the images. Newer methods can directly estimate depth by minimizing the regression loss, or by learning to generate a novel view from a sequence. The most popular benchmarks are KITTI and NYUv2. Models are typically evaluated according to a RMS metric.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/waterdisappear/sar-jepa
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
https://github.com/waterdisappear/sar-kpga
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
https://github.com/weihaox/ST-CLSTM
Exploiting temporal consistency for real-time video depth estimation
https://github.com/weihaox/UMBRAE
Brain Captioning: Decoding human brain activity into images and text
https://github.com/wen227/Pokemon_Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wenxinxu/resnet-in-tensorflow
https://github.com/wenxinxu/resnet_in_tensorflow
https://github.com/wfwf10/feature-based-federated-transfer-learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/winycg/MCL-OKD
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wkostuch/wild-style
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wonders19/Learning-Note
Deeper Depth Prediction with Fully Convolutional Residual Networks
https://github.com/wondonghyeon/protest-detection-violence-estimation
https://github.com/woongjoonchoi/DeepLearningPaper-Reproducing/tree/master/Vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/wrlife/RNN_depth_pose
Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depth
https://github.com/wuqiyao20160118/mini_project-for-CIFAR10
https://github.com/xcmyz/Face-Binary-Classification
https://github.com/xcmyz/FaceDetection
https://github.com/xcmyz/Face_Binary_Classfication
https://github.com/xhlulu/arxiv-assistant
https://github.com/xiandaguo/openstereo
https://github.com/xiaoqi-zhao-dlut/sslsod
https://github.com/xiaoyufenfei/LEDNet
https://github.com/xinkuansong/modified-resnet-acc-0.9638-10.7M-parameters
https://github.com/xiuyu0000/papers_with_examples/tree/main/cancer_detection_demo
https://github.com/xiuyu0000/tutorials/tree/main/resnet
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/resnet.py
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/vgg.py
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/xown3197/3D_Pedestrian_Localization_2021ComputerVision
Unsupervised Monocular Depth Estimation with Left-Right Consistency
https://github.com/xslidi/EfficientNets_ddl_apex
https://github.com/xufanxiong/classification-of-caltech-256
https://github.com/xuyingyue/DeepUnifiedCRF_iccv19
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
https://github.com/xy-guo/Learning-Monocular-Depth-by-Stereo
Learning Monocular Depth by Distilling Cross-domain Stereo Networks
https://github.com/yahoo/open_nsfw
https://github.com/yakhyo/EfficientNet-PyTorch
https://github.com/yakhyo/gaze-estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.