Global Clipper: Enhancing Safety and Reliability of Transformer-based
Object Detection Models

Global Clipper: Enhancing Safety and Reliability of Transformer-based Object Detection Models

5 June 2024

Qutub Syed Sha

Michael Paulitsch

Karthik Pattabiraman

Cornelius Buerkle

Kay-Ulrich Scholl

Papers citing "Global Clipper: Enhancing Safety and Reliability of Transformer-based Object Detection Models"

4 / 4 papers shown

Title
Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR Feng Li Ailing Zeng Siyi Liu Hao Zhang Hongyang Li Lei Zhang L. Ni ViT 36 67 0 13 Mar 2023
Soft Error Reliability Analysis of Vision Transformers Xing-xiong Xue Cheng Liu Ying Wang Bing Yang Tao Luo Lefei Zhang Huawei Li Xiaowei Li 39 14 0 21 Feb 2023
Hardware faults that matter: Understanding and Estimating the safety impact of hardware faults on object detection DNNs S. Qutub Florian Geissler Ya-ti Peng Ralf Gräfe Michael Paulitsch Gereon Hinz Alois C. Knoll AAML 29 5 0 07 Sep 2022
A Uniform Framework for Anomaly Detection in Deep Neural Networks Fangzhen Zhao Chenyi Zhang Naipeng Dong Zefeng You Zhenxin Wu AAML OOD OODD 30 9 0 06 Oct 2021