University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Visual Semantic Information Pursuit: A Survey

Liu, Daqi, Bober, Miroslaw and Kittler, Josef (2019) Visual Semantic Information Pursuit: A Survey IEEE Transactions on Pattern Analysis and Machine Intelligence.

Visual Semantic Information Pursuit A Survey.pdf - Accepted version Manuscript

Download (1MB) | Preview


Visual semantic information comprises two important parts: the meaning of each visual semantic unit and the coherent visual semantic relation conveyed by these visual semantic units. Essentially, the former one is a visual perception task while the latter one corresponds to visual context reasoning. Remarkable advances in visual perception have been achieved due to the success of deep learning. In contrast, visual semantic information pursuit, a visual scene semantic interpretation task combining visual perception and visual context reasoning, is still in its early stage. It is the core task of many different computer vision applications, such as object detection, visual semantic segmentation, visual relationship detection or scene graph generation. Since it helps to enhance the accuracy and the consistency of the resulting interpretation, visual context reasoning is often incorporated with visual perception in current deep end-to-end visual semantic information pursuit methods. Surprisingly, a comprehensive review for this exciting area is still lacking. In this survey, we present a unified theoretical paradigm for all these methods, followed by an overview of the major developments and the future trends in each potential direction. The common benchmark datasets, the evaluation metrics and the comparisons of the corresponding methods are also introduced.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Date : 30 October 2019
Funders : Defence Science and Technology Laboratory, Engineering and Physical Sciences Research Council (EPSRC)
DOI : 10.1109/TPAMI.2019.2950025
Grant Title : Multidisciplinary University Research Initiative
Copyright Disclaimer : © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
Uncontrolled Keywords : Semantic Scene Understanding; Visual Perception; Visual Context Reasoning; Deep Learning; Variational Free Energy Minimization; Message Passing; Visualization; Semantics; Task analysis; Cognition; Object detection
Depositing User : Clive Harris
Date Deposited : 07 Nov 2019 11:03
Last Modified : 07 Nov 2019 11:03

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800