Code for TIP2019 Progressive Learning for Person Re-Identification with One Example

Open source implementation of Adaptive Posterior Learning (ICLR 2019)

Open source implementation of Adaptive Posterior Learning (ICLR 2019)

Memory Networks implementations

The implementation of "The Kanerva Machine" with Pytorch and Pyro

This is a self-contained memory module for the Dynamic Kanerva Machine, as reported in the NIPS 2018 paper: Learning Attractor Dynamics for Generative Memory.

An implementation of DetNet: A Backbone network for Object Detection.

Code for our NeurIPS'19 paper "Learning Deep Bilinear Transformation for Fine-grained Image Representation"

Multi-layer convolutional LSTM with Pytorch

⛹️ Pytorch ReID: A tiny, friendly, strong pytorch implement of person re-id / vehicle re-id baseline. Tutorial 👉https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial

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

Open source person re-identification library in python

[ICCV 2019] "ABD-Net: Attentive but Diverse Person Re-Identification" https://arxiv.org/abs/1908.01114

Torchreid: Deep learning person re-identification in PyTorch.

2019全国人工智能大赛提交代码

👫 Joint Discriminative and Generative Learning for Person Re-identification. CVPR'19 (Oral) 👫

[AAAI 2019] Spatial Temporal Re-identification

Code for ECCV2018 paper: Part-Aligned Bilinear Representations for Person Re-Identification

Reproduce AlignedReID: Surpassing Human-Level Performance in Person Re-Identification, using Pytorch.

[NeurIPS-2018] FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification.

Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification CVPR 2019

SOTA Re-identification Methods and Toolbox

Saliency Weighted Convolutional Features for Instance Search

Class-Weighted Convolutional Features for Image Retrieval (BMVC 2017)

Cross-dimensional weighting for aggregated deep convolutional features.

Tensorflow code for ICML 2019 paper: LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning

Few-shot Object Detection via Feature Reweighting

Few-shot detection for visual categories