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acmmm21-vishv

Source codes of ACMMM21 paper: LearningHierarchicalEmbeddingsforVideoInstance Segmentation

actiontubes

source code for Finding Action Tubes, CVPR 2015

adaptiverp-tracking

Adaptive Region Proposal for Object Tracking

agame-vos

PyTorch implementation of the paper "A Generative Appearance Model for End-to-End Video Object Segmentation".

agnn

Zero-shot Video Object Segmentation via Attentive Graph Neural Networks (ICCV2019 Oral)

ags

(CVPR19) Learning Unsupervised Video Object Segmentation through Visual Attention

bingobjectness

BING Objectness proposal estimator Matlab wrapper. More in http://mmcheng.net/bing/

caffe

Caffe

carrierlxk.github.io

A source related to computer vision

cfbi

The official implementation of Collaborative Video Object Segmentation by Foreground-Background Integration.

cosnet

See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks (CVPR19)

dasiamrpn

Distractor-aware Siamese Networks for Visual Object Tracking (ECCV2018)

deformable-convnets

Deformable Convolutional Networks

dfn

dslt

Deep Regression Tracking with Shrinkage Loss (ECCV 2018).

emanet

The code for Expectation-Maximization Attention Networks for Semantic Segmentation (ICCV'2019 Oral)

faster_rcnn

Faster R-CNN

gnnpapers

Must-read papers on graph neural network (GNN)

gpytorch

A highly efficient and modular implementation of Gaussian Processes in PyTorch

graphmemvos

Video Object Segmentation with Episodic Graph Memory Networks (ECCV2020 spotlight)

lisa-caffe-public

Lisa Anne's public caffe code.

mttr

mug

Learning Video Object Segmentation from Unlabeled Videos (CVPR2020)

mylstm

LSTM practice

non-local_pytorch

Implementation of Non-local Block.

pattern-recognition-and-computer-vision

some simple codes about pattern recognition and computer vision

pumpkin-book

《机器学习》（西瓜书）公式推导解析，在线阅读地址：https://datawhalechina.github.io/pumpkin-book

py-dslt

pytorch implementation of Shrinkage loss in our ECCV paper 2018: Deep regression tracking with shrinkage loss

rcc

research code collection

reinspect

End to end detection in crowded scenes