Infinite-dimensional feature aggregation via a factorized bilinear model

Jindou Dai, Yuwei Wu*, Zhi Gao, Yunde Jia

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
Plum Print visual indicator of research metrics
  • Citations
    • Citation Indexes: 1
  • Captures
    • Readers: 2
see details

Abstract

Aggregating infinite-dimensional features has demonstrated superiority compared with their finite-dimensional counterparts. However, most existing methods approximate infinite-dimensional features with finite-dimensional representations, which inevitably results in approximation error and inferior performance. In this paper, we propose a non-approximate aggregation method that directly aggregates infinite-dimensional features rather than relying on approximation strategies. Specifically, since infinite-dimensional features are infeasible to store, represent and compute explicitly, we introduce a factorized bilinear model to capture pairwise second-order statistics of infinite-dimensional features as a global descriptor. It enables the resulting aggregation formulation to only involve the inner product in an infinite-dimensional space. The factorized bilinear model is calculated by a Sigmoid kernel to generate informative features containing infinite order statistics. Experiments on four visual tasks including the fine-grained, indoor scene, texture, and material classification, demonstrate that our method consistently achieves the state-of-the-art performance.

Original languageEnglish
Article number108397
JournalPattern Recognition
Volume124
DOIs
Publication statusPublished - Apr 2022

Keywords

  • Feature aggregation
  • Infinite-dimensional features
  • Non-approximate method
  • Second-order statistics

Fingerprint

Dive into the research topics of 'Infinite-dimensional feature aggregation via a factorized bilinear model'. Together they form a unique fingerprint.

Cite this

Dai, J., Wu, Y., Gao, Z., & Jia, Y. (2022). Infinite-dimensional feature aggregation via a factorized bilinear model. Pattern Recognition, 124, Article 108397. https://doi.org/10.1016/j.patcog.2021.108397