Algorithm

Engineering

Biology

Identification (biology)

Transfer of learning

Set (abstract data type)

Parallel computing

Computer science

Botany

Factor (programming language)

Transfer (computing)

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Domain (mathematical analysis)

Mathematical analysis

Artificial intelligence

Programming language

Fuse (electrical)

Machine learning

Electrical engineering

Recent deep learning based person re-identification approaches have steadily improved the performance for benchmarks, however they often fail to generalize well from one domain to another. In this work, we propose a novel adaptive transfer network (ATNet) for effective cross-domain person re-identification. ATNet looks into the essential causes of domain gap and addresses it following the principle of "divide-and-conquer". It decomposes the complicated cross-domain transfer into a set of factor-wise sub-transfers, each of which concentrates on style transfer with respect to a certain imaging factor, e.g., illumination, resolution and camera view etc. An adaptive ensemble strategy is proposed to fuse factor-wise transfers by perceiving the affect magnitudes of various factors on images. Such "decomposition-and-ensemble" strategy gives ATNet the capability of precise style transfer at factor level and eventually effective transfer across domains. In particular, ATNet consists of a transfer network composed by multiple factor-wise CycleGANs and an ensemble CycleGAN as well as a selection network that infers the affects of different factors on transferring each image. Extensive experimental results on three widely-used datasets, i.e., Market-1501, DukeMTMC-reID and PRID2011 have demonstrated the effectiveness of the proposed ATNet with significant performance improvements over state-of-the-art methods.

Adaptive Transfer Network for Cross-Domain Person Re-Identification