bobo-zz

bobo-zz's Stars

• sovrasov/flops-counter.pytorch

  Flops counter for convolutional networks in pytorch framework

  Language:Python2.8k1598305

• chengtan9907/OpenSTL

  OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning

  Language:Python81810156133

• xuhangc/ChatGPT-Academic-Prompt

  Use ChatGPT for academic writing

  5785070

• kobiso/CBAM-keras

  CBAM implementation on Keras

  Language:Python363710132

• pkumivision/FFC

  This is an official pytorch implementation of Fast Fourier Convolution.

  Language:Python35541332

• A4Bio/SimVP-Simpler-yet-Better-Video-Prediction

  The official implementation of the CVPR'2022 paper SimVP: Simpler Yet Better Video Prediction.

  Language:Python16221438

• JanMarcelKezmann/TensorFlow-Advanced-Segmentation-Models

  A Python Library for High-Level Semantic Segmentation Models based on TensorFlow and Keras with pretrained backbones.

  Language:Python15462649

• meteofrance/meteonet

  MeteoNet's toolbox and documentation

  Language:Jupyter Notebook12392123

• OSVAI/KernelWarehouse

  The official project website of "KernelWarehouse: Rethinking the Design of Dynamic Convolution" (KW for short, accepted to ICML 2024)

  Language:Python96435

• niecongchong/DANet-keras

  keras-Dual Attention Network for Scene Segmentation

  Language:Jupyter Notebook685833

• FengheTan9/CMU-Net

  [ISBI 2023] Official Pytorch implementation of "CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network"

  Language:Python65295

• ethanygao/NDDR-CNN

  [CVPR2019] NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction

  Language:Python54426

• wandahangFY/MLCA

  The codes of MLCA.

  Language:Python52123

• joshua-wx/pyhail

  Hail detection and size retrievals using Python3.9+

  Language:Jupyter Notebook4317615

• el3ment/spectral_representations_for_convolutional_neural_networks

  Language:Python415510

• zhouh115/DWT-FFC

  Official PyTorch implementation of dehazing method based on FFC and ConvNeXt, 1st place solution of NTIRE 2023 HR NonHomogeneous Dehazing Challenge (CVPR Workshop 2023).

  Language:Python40153

• innat/VideoSwin

  Keras 3 Implementation of Video Swin Transformers for 3D Video Modeling

  Language:Jupyter Notebook28264

• x7zhong/TransfomerDownscaling

  This includes the code and data used in the paper "Investigating transformer-based models for downscaling near-surface temperature and wind speed".

  Language:Python18213

• JinLi711/Convolution_Variants

  Reimplementing SOTA convolution variants with Tensorflow 2.0.

  Language:Python17314

• EricPengShuai/Pytorch-Learning

  Pytorch Framework learning for deeplearning

  Language:Jupyter Notebook14102

• zzr-idam/Spiking-Neural-Network-Image-Restoration

  Image restoration using spiking neural networks.

  Language:Python11221

• niecongchong/Remote-sensing-principle-and-application

  科普总结遥感成像原理，数据处理以及实际应用！

  10001

• sascha-kirch/DeepSaki

  DeepSaki is an add-on to TensorFlow. It provides a variaty of custom classes ranging from activation functions to entire models, helper functions to facilitate connectiong to your, compute HW and many more!

  Language:Python102140

• bmdeep/SegPC2021

  Multi-scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Images

  Language:Jupyter Notebook8623

• sunhang1986/MFINEA

  Language:Python5110

• snehashismajhi/Temporal-Pooling-in-Inflated-3DCNN-for-Weakly-supervised-Video-Anomaly-Detection

  Anomaly detection in surveillance videos requires significant attention in feature engineering to discriminate anomaly activity patterns from normal patterns. Keeping this in mind, this paper aims to extract superior quality spatio temporal features from Inflated 3DCNN followed by a temporal pooling strategy to intensify relevant spatio temporal feature in untrimmed anomalous videos. A superior temporal pooling strategy leads to better understanding of temporal dependency through LSTM model, which has become a necessary step for anomaly detection in surveillance videos. Thus, we propose a method consisting of an ideal temporal pooling strategy in inflated 3DCNN feature map along with LSTM model for temporal dependency encoding for weakly-supervised anomaly detection task. Our method is validated on a large scale video anomaly detection dataset, namely UCF-crime, resulting competitive performance in anomaly detection task with recent state-of-the-art methodologies.

  Language:Python4101

• 92xianshen/refined-unet-v3

  Language:Python3311

• PraveerT/3DCNN-LSTM

  Driving code for 3DCNN-LSTM for radar

  Language:Python3100

• ihznuy32/FREM-Dataset

  Language:Python2101

• TimeFrequency/TFFF

  Time-frequency feature local fusion and global fusion

  Language:Python2100