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pro vyhledávání: '"Foteinopoulou, Niki Maria"'
Explainability in artificial intelligence is crucial for restoring trust, particularly in areas like face forgery detection, where viewers often struggle to distinguish between real and fabricated content. Vision and Large Language Models (VLLM) brid
Externí odkaz:
http://arxiv.org/abs/2410.00485
Autor:
Xenos, Alexandros, Foteinopoulou, Niki Maria, Ntinou, Ioanna, Patras, Ioannis, Tzimiropoulos, Georgios
Recognising emotions in context involves identifying the apparent emotions of an individual, taking into account contextual cues from the surrounding scene. Previous approaches to this task have involved the design of explicit scene-encoding architec
Externí odkaz:
http://arxiv.org/abs/2404.07078
Schizophrenia is a severe yet treatable mental disorder, it is diagnosed using a multitude of primary and secondary symptoms. Diagnosis and treatment for each individual depends on the severity of the symptoms, therefore there is a need for accurate,
Externí odkaz:
http://arxiv.org/abs/2310.16677
Facial Expression Recognition (FER) is a crucial task in affective computing, but its conventional focus on the seven basic emotions limits its applicability to the complex and expanding emotional spectrum. To address the issue of new and unseen emot
Externí odkaz:
http://arxiv.org/abs/2310.16640
Human affect and mental state estimation in an automated manner, face a number of difficulties, including learning from labels with poor or no temporal resolution, learning from few datasets with little data (often due to confidentiality constraints)
Externí odkaz:
http://arxiv.org/abs/2207.05577