Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification CVPR 2019
The main features of zhunzhong07/ecn are: Person Re-identification.
Open-source alternatives to zhunzhong07/ecn include: kaiyangzhou/deep-person-reid — This project is a PyTorch person re-identification framework designed for training and evaluating models that identify… nwojke/deep_sort — DeepSORT is a real-time multi-object tracking framework designed to maintain consistent identities of multiple objects… thuml/transfer-learning-library — This project is a comprehensive library for transfer learning and domain adaptation in computer vision. It serves as a… prasannapulakurthi/foreground-background-augmentation — ReID + SFDA - Dual-Region Augmentation (Foreground Noise + Background Shuffle) for robust person re-identification… helioszhao/m3l — Pytorch implementation for M^3L. CVPR 2021. yxgeee/mmt — [ICLR-2020] Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification.
This project is a PyTorch person re-identification framework designed for training and evaluating models that identify individuals across different camera views. It provides a complete model training pipeline, a deep learning feature extractor for converting images into numeric vectors, and a suite of computer vision benchmarking tools to measure identity retrieval accuracy. The framework includes a specialized transfer learning toolkit that supports layer freezing, staged learning rate optimization, and differential learning rates for fine-tuning pretrained models. It distinguishes itself th
DeepSORT is a real-time multi-object tracking framework designed to maintain consistent identities of multiple objects across video frames. It integrates deep learning appearance features with motion descriptors to track objects through a sequence of video data. The system uses a deep convolutional neural network to generate high-dimensional visual descriptors for person re-identification. These appearance features are combined with motion estimation via Kalman filtering and solved using the Hungarian algorithm to optimally associate detections with existing tracks. The framework includes ca
This project is a comprehensive library for transfer learning and domain adaptation in computer vision. It serves as a framework for aligning feature distributions between source and target datasets, a toolkit for domain generalization, and a library for semi-supervised learning using small labeled datasets and large unlabeled sets. The library provides specialized capabilities for unsupervised domain adaptation, including the use of adversarial networks, discrepancy-based architectures, and image-to-image translation to reduce distribution mismatch. It also includes tools for domain generali
ReID SFDA - Dual-Region Augmentation (Foreground Noise Background Shuffle) for robust person re-identification (ReID) and source-free domain adaptation (SFDA).