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arXiv Computer Vision By Eugene Denteh, Blessing Agyei Kyem, Andrews Danyo, Joshua Kofi Asamoah, Richard Dzinyela, Solomon Owusu-Ansah, Dickson G. Asiedu, Armstrong Aboah

Synthetic-to-Real Text-Based Person Anomaly Search via Multi-Model Retrieval Fusion and Cross-Encoder Reranking

Read the original on arXiv Computer Vision →

The paper introduces a multi‑model retrieval and reranking system for text‑based person anomaly search that bridges the synthetic‑to‑real domain gap. It adapts the Qwen3‑VL‑Embedding‑8B model to synthetic descriptions via contrastive learning and domain randomization, fuses scores from six Qwen3‑VL checkpoints and a frozen SigLIP2 model, and then reranks candidates with a Qwen3‑VL‑Reranker‑8B cross‑encoder. A synthetic distractor‑matched validation protocol is used to tune hyper‑parameters, yielding a 94.78% mAP and 90.85% R@1 on the AI City Challenge 2026 Track 4 test set, placing eighth on the public leaderboard.

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 Computer Vision.

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