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lzh990711's Projects

actionvlad icon actionvlad

ActionVLAD for video action classification (CVPR 2017)

adabelief-optimizer icon adabelief-optimizer

Repository for NeurIPS 2020 Spotlight "AdaBelief Optimizer: Adapting stepsizes by the belief in observed gradients"

algorithm_interview_notes-chinese icon algorithm_interview_notes-chinese

2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记

alphapose icon alphapose

Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System

batch-dropblock-network icon batch-dropblock-network

Official source code of "Batch DropBlock Network for Person Re-identification and Beyond" (ICCV 2019)

bisenet icon bisenet

Add bisenetv2. My implementation of BiSeNet

bottom-up-attention icon bottom-up-attention

Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome

caffe icon caffe

Caffe: a fast open framework for deep learning.

caffe-1 icon caffe-1

modified version of caffe which support DeconvNet and DecoupledNet

caffe-segnet icon caffe-segnet

Implementation of SegNet: A Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-Wise Labelling

caffe-segnet-cudnn5 icon caffe-segnet-cudnn5

This repository was a fork of BVLC/caffe and includes the upsample, bn, dense_image_data and softmax_with_loss (with class weighting) layers of caffe-segnet (https://github.com/alexgkendall/caffe-segnet) to run SegNet with cuDNN version 5.

caffe_mod icon caffe_mod

latest caffe official release + transpose layer

circleloss icon circleloss

Pytorch implementation of the paper "Circle Loss: A Unified Perspective of Pair Similarity Optimization"

crfasrnn icon crfasrnn

This repository contains the source code for the semantic image segmentation method described in the ICCV 2015 paper: Conditional Random Fields as Recurrent Neural Networks. http://crfasrnn.torr.vision/

crfasrnn-merge icon crfasrnn-merge

This is a repository forked from the latest official caffe-master on Jun 23, 2016. And I merged the latest code of CRFasRNN with it. I have simply tested the code, and it worked right. But there is no guarantee for the merge.

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