English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recent studies on pedestrian attribute recognition progress with eitherexplicit or implicit modeling of the co-occurrence among attributes.Considering that this known a prior is highly variable and unforeseeableregarding the specific scenarios, we show that current methods can actuallysuffer in generalizing such fitted attributes interdependencies onto scenes oridentities off the dataset distribution, resulting in the underlined bias ofattributes co-occurrence. To render models robust in realistic scenes, wepropose the attributes-disentangled feature learning to ensure the recognitionof an attribute not inferring on the existence of others, and which issequentially formulated as a problem of mutual information minimization.Rooting from it, practical strategies are devised to efficiently decoupleattributes, which substantially improve the baseline and establishstate-of-the-art performance on realistic datasets like PETAzs and RAPzs. Codeis released onhttps://github.com/SDret/A-Solution-to-Co-occurence-Bias-in-Pedestrian-Attribute-Recognition.

A Solution to Co-occurrence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute Recognition