w64228013

w64228013

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AnomalyHop

Code for AnomalyHop

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Awesome-Visual-Transformer

Collect some papers about transformer with vision. Awesome Transformer with Computer Vision (CV)

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ccf-deadlines

⏰ CCF recommendation conference Deadline Countdowns / Please star this project, thanks~

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DarkLabel

Video/Image Labeling and Annotation Tool

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DenseUNet-pytorch

A Pytorch implementation of U-Net using a DenseNet-121 backbone

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DFCAN-pytorch

Code is basically a pytorch implementation of DFCAN/DFGAN. All credit goes to the authors of DFCAN/DFGAN, listed in the paper“Evaluation and development of deep neural networks for image super-resolution in optical microscopy”.

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DFMGAN

[AAAI 2023] Official implementation of "Few-Shot Defect Image Generation via Defect-Aware Feature Manipulation".

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DL-SR

Tensorflow/keras implementation for image transformation from low-resolution (LR) image to super-resolved one, including single wide-field (WF) image super-resolution prediction and SIM reconstruction.

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face_recognition

The world's simplest facial recognition api for Python and the command line

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LabelTrack

LabelTrack是一个针对于多目标跟踪的图形化自动标注平台

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MAML

[MICCAI 2021] The official code for "Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation"

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MER2023-Baseline

Baseline code for MER 2023 http://merchallenge.cn/mer2023

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mmdetection

OpenMMLab Detection Toolbox and Benchmark

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pretrained-models.pytorch

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

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RobustVideoMatting

Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!

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TBraTS

【MICCAI 2022】"TBraTS: Trusted Brain Tumor Segmentation"

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transfuser

[CVPR'21] Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

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UMIS

EvidenceCap: Towards trustworthy medical image segmentation via evidential identity cap

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Visualizer

helper tools for attention visualization in deep learning

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Zero-Shot-Emotion-Recognition-with-AAE

Learning Unseen Emotions from Gestures via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders

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