Pre-trained priors from foundation models drive substantial performance variance in person re-identification, with simple fine-tuning of large models reaching SOTA results while staying close to initial parameters.
The p-destre: A fully an- notated dataset for pedestrian detection, tracking, and short/long-term re-identification from aerial devices
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Not All Starting Points Are Equal: Pre-trained Priors and Their Outsized Impact on Person Identification
Pre-trained priors from foundation models drive substantial performance variance in person re-identification, with simple fine-tuning of large models reaching SOTA results while staying close to initial parameters.