Person Re-Identification via Active Hard Sample Mining

Understand

Annotating a large-scale image dataset is very tedious, yet necessary for training person re-identification models.

• To alleviate such a problem, we present an active hard sample mining framework via training an effective re-ID model with the least labeling efforts.
• Considering that hard samples can provide informative patterns, we first formulate an uncertainty estimation to actively select hard samples to iteratively train a re-ID model from scratch.
• Then, intra-diversity estimation is designed to reduce the redundant hard samples by maximizing their diversity.