Probabilistic latent semantic analysis

Identification (biology)

Feature selection

Pattern recognition (psychology)

Feature (linguistics)

Block (permutation group theory)

Mathematics

Biology

Politics

Discriminative model

Machine learning

Philosophy

Political science

Artificial intelligence

Programming language

Geometry

Annotation

Representation (politics)

Factor (programming language)

Visualization

Computer science

Linguistics

Botany

Object (grammar)

Feature learning

Selection (genetic algorithm)

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.

Multi-level Factorisation Net for Person Re-identification