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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Explainable AI (XAI) methods are frequently applied to obtain qualitativeinsights about deep models' predictions. However, such insights need to beinterpreted by a human observer to be useful. In this paper, we aim to useexplanations directly to make decisions without human observers. We adopt twogradient-based explanation methods, Integrated Gradients (IG) and backprop, forthe task of 3D object detection. Then, we propose a set of quantitativemeasures, named Explanation Concentration (XC) scores, that can be used fordownstream tasks. These scores quantify the concentration of attributionswithin the boundaries of detected objects. We evaluate the effectiveness of XCscores via the task of distinguishing true positive (TP) and false positive(FP) detected objects in the KITTI and Waymo datasets. The results demonstratean improvement of more than 100\% on both datasets compared to other heuristicssuch as random guesses and the number of LiDAR points in the bounding box,raising confidence in XC's potential for application in more use cases. Ourresults also indicate that computationally expensive XAI methods like IG maynot be more valuable when used quantitatively compare to simpler methods.

XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection