https://github.com/Mind23-2/MindCode-31
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
Depth Estimation
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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/Mind23-2/MindCode-4/tree/main/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mind23-2/MindCode-57
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
https://github.com/Mind23-2/MindCode-67
https://github.com/Mind23-2/MindCode-70
https://github.com/MindCode-4/code-11/tree/main/auxiliary-tasks-in-multi-task-learning
https://github.com/MindCode-4/code-13/tree/main/robustness_depth_lang
On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth Estimation
https://github.com/MindCode-4/code-3/tree/main/depth_anything
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
https://github.com/MindCode-4/code-4/tree/main/rembert
https://github.com/MindCode-4/code-6/tree/main/auxiliary-tasks-in-multi-task-learning
https://github.com/MindCode-4/code-7/tree/main/factorized-attention
https://github.com/MindSpore-MS-Code2/code0/tree/main/DepthNet
VA-DepthNet: A Variational Approach to Single Image Depth Prediction
https://github.com/MindSpore-paper-code-2/code2/tree/main/PoseNet
Towards Better Generalization: Joint Depth-Pose Learning without PoseNet
https://github.com/MindSpore-paper-code-2/code2/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MindSpore-paper-code-2/code2/tree/main/RefineNet
Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations
https://github.com/MindSpore-paper-code-2/code3/tree/main/STPM
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
https://github.com/MindSpore-paper-code-2/code3/tree/main/ssd_resnet34
https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code5/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnet50_adv_pruning
https://github.com/MindSpore-paper-code-3/code6/tree/main/ADNet
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code8/tree/main/DepthNet
S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceAttribute
https://github.com/MindSpore-scientific/code-5/tree/main/robustness_depth_lang
On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth Estimation
https://github.com/MindSpore-scientific/code-7/tree/main/GroupViT
ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation
https://github.com/MioChiu/ResNet_TensorFlow
https://github.com/MisaOgura/flashtorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MishaLaskin/vq-vae
https://github.com/MishaLaskin/vqvae
https://github.com/MrRiahi/Convolutional-Neural-Networks-Tensorflow
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/NASA-NeMO-Net/NeMO-Net
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/NSR9/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/NVIDIA-AI-IOT/redtail
https://github.com/NVIDIA/retinanet-examples
https://github.com/Navhkrin/Bts-PyTorch
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.