Multi-Level Factorisation Net for Person Re-Identification
This addresses the challenge of accurate person re-identification for surveillance and security applications, offering a novel method that improves performance without requiring laborious human annotation.
The paper tackles the problem of person re-identification by modeling discriminative and view-invariant factors at multiple semantic levels without manual annotation, achieving state-of-the-art results on three Re-ID datasets and compelling results on CIFAR-100.
Key to effective person re-identification (Re-ID) is modelling discriminative and view-invariant factors of person appearance at both high and low semantic levels. Recently developed deep Re-ID models either learn a holistic single semantic level feature representation and/or require laborious human annotation of these factors as attributes. We propose Multi-Level Factorisation Net (MLFN), a novel network architecture that factorises the visual appearance of a person into latent discriminative factors at multiple semantic levels without manual annotation. MLFN is composed of multiple stacked blocks. Each block contains multiple factor modules to model latent factors at a specific level, and factor selection modules that dynamically select the factor modules to interpret the content of each input image. The outputs of the factor selection modules also provide a compact latent factor descriptor that is complementary to the conventional deeply learned features. MLFN achieves state-of-the-art results on three Re-ID datasets, as well as compelling results on the general object categorisation CIFAR-100 dataset.