Front-to-Back: Benchmarking Vision-Language Models for Asymmetric Cross-View Vehicle Re-Identification

Name: Front-to-Back: Benchmarking Vision-Language Models for Asymmetric Cross-View Vehicle Re-Identification
Uploaded: 2026-10-04T22:47:48.135192+00:00
Duration: 261 s
Description: This work introduces Front2Back-ReID, a benchmark testing zero-shot vision-language models (VLMs) on matching vehicles across front and rear cameras where views are different. It compares VLMs against retrieval baselines and humans using three input conditions: full RGB, target crops, and binary silhouettes to evaluate performance under various difficulty settings.

This work introduces Front2Back-ReID, a benchmark testing zero-shot vision-language models (VLMs) on matching vehicles across front and rear cameras where views are different. It compares VLMs against retrieval baselines and humans using three input conditions: full RGB, target crops, and binary silhouettes to evaluate performance under various difficulty settings.

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