Song Bai's repositories

Adversarial_Metric_Attack

The code of "Adversarial Metric Attack for Person Re-identification"

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Sparse-Contextual-Activation

The matlab code of Sparse Contextual Activation (SCA) published in TIP 2016

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afm_cvpr2019

Official implementation of paper "Learning Attraction Field Map for Robust Line Segment Detection" (CVPR 2019)

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AlignPS

Code for CVPR 2021 paper: Anchor-Free Person Search

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awesome-semantic-segmentation

:metal: awesome-semantic-segmentation

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beyond-part-models

PCB of paper: Beyond Part Models: Person Retrieval with Refined Part Pooling, using Pytorch

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CenterNet

Codes for our paper "CenterNet: Keypoint Triplets for Object Detection" .

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cleverhans

An adversarial example library for constructing attacks, building defenses, and benchmarking both

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deep-person-reid

Pytorch implementation of deep person re-identification models.

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DI-2-FGSM

Improving Transferability of Adversarial Examples with Input Diversity

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MVCNN-TensorFlow

An Multi-View CNN (MVCNN) implementation with TensorFlow.

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oicr

Codes for our paper "Multiple Instance Detection Network with Online Instance Classifier Refinement" and "PCL: Proposal Cluster Learning for Weakly Supervised Object Detection".

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open-reid

Open source person re-identification library in python

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Person_reID_baseline_pytorch

Pytorch implement of Person re-identification baseline. We arrived Rank@1=88.24%, mAP=70.68% only with softmax loss. Re-ranking is added.

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pytorch-semantic-segmentation

PyTorch for Semantic Segmentation

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Regional-Homogeneity

Source code for Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses

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tensorflow

Computation using data flow graphs for scalable machine learning

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tensorflow-models

Models built with TensorFlow

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tf-eval-reporter

Tensorflow "layer" that categorizes good and bad predictions into a HTML document

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TF_FeatureExtraction

Convenient wrapper for TensorFlow feature extraction from pre-trained models using tf.contrib.slim

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triplet-reid

Code for reproducing the results of our "In Defense of the Triplet Loss for Person Re-Identification" paper.

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