Trustworthy Large Models in Vision: A Survey

• URL: http://arxiv.org/abs/2311.09680v5
• Date: Thu, 1 Feb 2024 05:15:52 GMT
• Title: Trustworthy Large Models in Vision: A Survey
• Authors: Ziyan Guo and Li Xu and Jun Liu
• Abstract summary: Large Models (LMs) have revolutionized various fields of deep learning, including Natural Language Processing (NLP) to Computer Vision (CV) LMs are increasingly challenged and criticized by academia and industry due to their powerful performance but untrustworthy behavior. We summarize four relevant concerns that obstruct the trustworthy usage in vision of LMs in this survey, including 1) human misuse, 2) vulnerability, 3) inherent issue and 4) interpretability. We hope this survey will facilitate readers' understanding of this field, promote alignment of LMs with human expectations and enable trustworthy LMs to serve as welfare rather than disaster for human society.
• Score: 8.566163225282724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid progress of Large Models (LMs) has recently revolutionized various fields of deep learning with remarkable grades, ranging from Natural Language Processing (NLP) to Computer Vision (CV). However, LMs are increasingly challenged and criticized by academia and industry due to their powerful performance but untrustworthy behavior, which urgently needs to be alleviated by reliable methods. Despite the abundance of literature on trustworthy LMs in NLP, a systematic survey specifically delving into the trustworthiness of LMs in CV remains absent. In order to mitigate this gap, we summarize four relevant concerns that obstruct the trustworthy usage in vision of LMs in this survey, including 1) human misuse, 2) vulnerability, 3) inherent issue and 4) interpretability. By highlighting corresponding challenge, countermeasures, and discussion in each topic, we hope this survey will facilitate readers' understanding of this field, promote alignment of LMs with human expectations and enable trustworthy LMs to serve as welfare rather than disaster for human society.

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