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cs.CV2023
Driving through the Concept Gridlock: Unraveling Explainability Bottlenecks in Automated Driving
Jessica Echterhoff, An Yan, Kyungtae Han +3
Concept bottleneck models have been successfully used for explainable machine learning by encoding information within the model with a set of human-defined concepts. In the context…
cs.CV2023
MDAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer
Yunsheng Ma, Liangqi Yuan, Amr Abdelraouf +4
Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. I…
cs.CV2022
Contrastive Self-Supervised Learning Leads to Higher Adversarial Susceptibility
Rohit Gupta, Naveed Akhtar, Ajmal Mian +1
Contrastive self-supervised learning (CSL) has managed to match or surpass the performance of supervised learning in image and video classification. However, it is still largely un…