multimodal
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arXiv:2411.19939v2 Announce Type: replace Abstract: Safety concerns of Multimodal large language models (MLLMs) have gradually become an important problem in various applications. Surprisingly, previous works indicate a counter-intuitive phenomenon that using textual unlearning to align MLLMs achieves comparable safety performances with MLLMs trained with image-text pairs. To explain such a counter-intuitive phenomenon, we discover a…
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submitted by /u/mymalema [link] [comments]
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[Submitted on 30 Nov 2024 (v1), last revised 4 Dec 2024 (this version, v2)] View a PDF of the paper titled MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption, by Siddhant Dutta and 4 other authors
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5 Small-Scale Multimodal AI Models and What They Can Do – The New Stack
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Tech giant NEC has developed a new technology that allows authentication with both face and iris biometrics with a single camera image. The solution allows iris recognition even with lower-resolution photos that contain a lot of noise, taken with a camera used for facial recognition.
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By Vignesh Krishnakumar, co-founder and CTO of HyperVerge
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MalBot October 1, 2024, 4:55pm 1 Sophos’ Younghoo Lee will present his research on the use of AI to analyze both text and image data to classify spam, phishing, and unsafe web content in Dublin.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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[Submitted on 24 Feb 2024] Download a PDF of the paper titled Cryptanalysis and improvement of multimodal data encryption by machine-learning-based system, by Zakaria Tolba
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.