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

Candidate: Sun Sheng Gu
Title: XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection
Date: December 22, 2021
Time: 13:00
Place: online
Supervisor(s): Czarnecki, Krzysztof

Abstract:
Explainable AI (XAI) methods are frequently applied to obtain qualitative insights
about deep models’ predictions. However, such insights need to be interpreted by a human
observer to be useful. In this thesis, we aim to use explanations directly to make decisions
without human observers. We adopt two gradient-based explanation methods, Integrated
Gradients (IG) and backprop, for the task of 3D object detection. Then, we propose a
set of quantitative measures, named Explanation Concentration (XC) scores, that can be
used for downstream tasks. These scores quantify the concentration of attributions within
the boundaries of detected objects. We evaluate the effectiveness of XC scores via the task
of distinguishing true positive (TP) and false positive (FP) detected objects in the KITTI
and Waymo datasets. The results demonstrate improvement of more than 100% on both
datasets compared to other heuristics such as random guesses and number of LiDAR points
in bounding box, raising confidence in XC’s potential for application in more use cases.
Our results also indicate that computationally expensive XAI methods like IG may not be
more valuable when used quantitatively compared to simpler methods. Moreover, we apply
loss terms based on XC and pixel attribution prior (PAP), which is another qualitative
measure for attributions, to the task of training a 3D object detection model. We show
that performance boost is possible as long as we select the right subset of predictions for
which the attribution-based losses are applied.