Empirical study of a fully synthetic data generation pipeline for text-based person retrieval that tests its use as a replacement or augmentation for real data across scenarios.
Pose-dIVE: Pose-Diversified Augmentation with Diffusion Model for Person Re-Identification
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Person re-identification (Re-ID) often faces challenges due to variations in human poses and camera viewpoints, which significantly affect the appearance of individuals across images. Existing datasets frequently lack diversity and scalability in these aspects, hindering the generalization of Re-ID models to new camera systems or environments. To overcome this, we propose Pose-dIVE, a novel data augmentation approach that incorporates sparse and underrepresented human pose and camera viewpoint examples into the training data, addressing the limited diversity in the original training data distribution. Our objective is to augment the training dataset to enable existing Re-ID models to learn features unbiased by human pose and camera viewpoint variations. By conditioning the diffusion model on both the human pose and camera viewpoint through the SMPL model, our framework generates augmented training data with diverse human poses and camera viewpoints. Experimental results demonstrate the effectiveness of our method in addressing human pose bias and enhancing the generalizability of Re-ID models compared to other data augmentation-based Re-ID approaches.
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SD-ReID trains a ViT to extract identity and view conditions, fine-tunes Stable Diffusion to generate view-mimicking features, adds a View-Refined Decoder, and combines both identity and all-view features for retrieval on aerial-ground re-identification benchmarks.
ID-Sim is a new similarity metric that aims to capture human selective sensitivity to identities by training on curated real and generative synthetic data and validating against human annotations on recognition, retrieval, and generative tasks.
citing papers explorer
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An Empirical Study of Validating Synthetic Data for Text-Based Person Retrieval
Empirical study of a fully synthetic data generation pipeline for text-based person retrieval that tests its use as a replacement or augmentation for real data across scenarios.
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SD-ReID: View-aware Stable Diffusion for Aerial-Ground Person Re-Identification
SD-ReID trains a ViT to extract identity and view conditions, fine-tunes Stable Diffusion to generate view-mimicking features, adds a View-Refined Decoder, and combines both identity and all-view features for retrieval on aerial-ground re-identification benchmarks.
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ID-Sim: An Identity-Focused Similarity Metric
ID-Sim is a new similarity metric that aims to capture human selective sensitivity to identities by training on curated real and generative synthetic data and validating against human annotations on recognition, retrieval, and generative tasks.