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arXiv Machine Learning By Karina Kvanchiani, Timur Mamedov

Analyzing the Optimization Conflict Between Image- and Text-Based Person Re-Identification

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The paper investigates the conflict between image‑based (I2I) and text‑based (T2I) person re‑identification objectives when trained on a shared vision encoder. It shows that optimizing both simultaneously degrades performance because I2I contracts intra‑identity distances while T2I preserves them. A two‑stage pipeline—first training the encoder for I2I invariance and then freezing it while aligning the text encoder—improves T2I results, suggesting a path toward unified ReID systems.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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