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

Scene-understanding is an important topic in the area of Computer Vision, andillustrates computational challenges with applications to a wide range ofdomains including remote sensing, surveillance, smart agriculture, robotics,autonomous driving, and smart cities. We consider the active explanation-drivenunderstanding and classification of scenes. Suppose that an agent utilizing oneor more sensors is placed in an unknown environment, and based on its sensoryinput, the agent needs to assign some label to the perceived scene. The agentcan adjust its sensor(s) to capture additional details about the scene, butthere is a cost associated with sensor manipulation, and as such, it isimportant for the agent to understand the scene in a fast and efficient manner.It is also important that the agent understand not only the global state of ascene (e.g., the category of the scene or the major events taking place in thescene) but also the characteristics/properties of the scene that supportdecisions and predictions made about the global state of the scene. Finally,when the agent encounters an unknown scene category, it must be capable ofrefusing to assign a label to the scene, requesting aid from a human, andupdating its underlying knowledge base and machine learning models based onfeedback provided by the human. We introduce a dynamic data driven frameworkfor the active explanation-driven classification of scenes. Our framework isentitled ACUMEN: Active Classification and Understanding Method byExplanation-driven Networks. To demonstrate the utility of the proposed ACUMENapproach and show how it can be adapted to a domain-specific application, wefocus on an example case study involving the classification of indoor scenesusing an active robotic agent with vision-based sensors, i.e., anelectro-optical camera.

A Dynamic Data Driven Approach for Explainable Scene Understanding