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ReID + SFDA - Dual-Region Augmentation (Foreground Noise + Background Shuffle) for robust person re-identification (ReID) and source-free domain adaptation (SFDA).
The main features of prasannapulakurthi/foreground-background-augmentation are: Multi-Target Adaptation, Person Re-identification, Source Free Domain Adaptation.
Projects with overlapping indexed features include: vcl-iisc/conmix — Source free Single and Multi target Unsupervised Domain Adaptation. prasannapulakurthi/spm — Shuffle PatchMix (SPM) for Source-Free Domain Adaptation (ICIP 2025); patch-shuffle augmentation + confidence-margin… kaiyangzhou/deep-person-reid — This project is a PyTorch person re-identification framework designed for training and evaluating models that identify… thuml/transfer-learning-library — This project is a comprehensive library for transfer learning and domain adaptation in computer vision. It serves as a… nwojke/deep_sort — DeepSORT is a real-time multi-object tracking framework designed to maintain consistent identities of multiple objects… helioszhao/m3l — Pytorch implementation for M^3L. CVPR 2021.
Source free Single and Multi target Unsupervised Domain Adaptation
Shuffle PatchMix (SPM) for Source-Free Domain Adaptation (ICIP 2025); patch-shuffle augmentation confidence-margin pseudo-labels. New SOTA on PACS (+7.3%), strong results on DomainNet-126 and VisDA-C.
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