(ImageNet pretrained models) The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"
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Updated
Dec 8, 2022 - Python
(ImageNet pretrained models) The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"
An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).
Project page of the paper "Learning Multi-Scale Photo Exposure Correction" (CVPR 2021).
[TVCG'2023] AOT-GAN for High-Resolution Image Inpainting (codebase for image inpainting)
PyTorch implementation of Octave Convolution with pre-trained Oct-ResNet and Oct-MobileNet models
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation in CVPR 2017 (Spotlight)
Open Scripts and pipelines from the Multimodal Imaging and Connectome Analysis Lab at the Montreal Neurological Institute
[ECCV 2020 Spotlight] A Simple and Versatile Framework for Image-to-Image Translation
[ECCV 2020] PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer
The implementation of FDCNN in paper - A Feature Difference Convolutional Neural Network-Based Change Detection Method
Fully supervised binary classification of skin lesions from dermatoscopic images using an ensemble of diverse CNN architectures (EfficientNet-B6, Inception-V3, SEResNeXt-101, SENet-154, DenseNet-169) with multi-scale input.
Res2Net for Panoptic Segmentation based on detectron2 (SOTA results).
Multi-scale network for image deblurring
Implementation of "SpEx: Multi-Scale Time Domain Speaker Extraction Network".
A distributed implementation of "Nested Subtree Hash Kernels for Large-Scale Graph Classification Over Streams" (ICDM 2012).
Semantic Labeling in VHR Images via A Self-Cascaded CNN (ISPRS JPRS, IF=6.942)
PyTorch implementation of "Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration"
The MultiScale Network for hierarchical regression (MS-Net) performs 3D regression based on a hierarchical principle: coarse inputs provide broad information about the data, and progressively finer-scale inputs can be used to refine this information.
MPI-based code for distributed HPC simulations with the sparse grid combination technique
Accompaniment code for 'Hilbert sEMG data scanning for hand gesture recognition based on Deep Learning' published in NCAA.
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