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Found 3 papers, 1 papers with code
Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification
Moreover, aiming to fully exploit the potential of FineGPR and promote the efficient training from millions of synthetic data, we propose an attribute analysis pipeline called AOST, which dynamically learns attribute distribution in real domain, then eliminates the gap between synthetic and real-world data and thus is freely deployed to new scenarios.
Attribute analysis with synthetic dataset for person re-identification
To address this problem, firstly, we develop a large-scale synthetic data engine, the salient characteristic of this engine is controllable.
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