Combining Task Predictors via Enhancing Joint Predictability

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Colleges, School and Institutes

External organisations

  • UNIST, Korea
  • University of Bath


Predictor combination aims to improve a (target) predictor of a learning task based on the (reference) predictors of potentially relevant tasks, without having access to the internals of individual predictors. We present a new predictor combination algorithm that improves the target by i) measuring the relevance of references based on their capabilities in predicting the target, and ii) strengthening such estimated relevance. Unlike existing predictor combination approaches that only exploit pairwise relationships between the target and each reference, and thereby ignore potentially useful dependence among references, our algorithm jointly assesses the relevance of all references by adopting a Bayesian framework. This also offers a rigorous way to automatically select only relevant references. Based on experiments on seven real-world datasets from visual attribute ranking and multi-class classification scenarios, we demonstrate
that our algorithm offers a significant performance gain and broadens the application range of existing predictor combination approaches.

Bibliographic note

Publication due November 2020


Original languageEnglish
Title of host publicationComputer Vision - ECCV 2020
Subtitle of host publication16th European Conference, Glasgow, UK, Agust 23-28 2020, Proceedings
Publication statusAccepted/In press - 3 Jul 2020
Event16th European Conference on Computer Vision (ECCV2020) - Virtual Event
Duration: 23 Aug 202028 Aug 2020

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th European Conference on Computer Vision (ECCV2020)
CityVirtual Event