The purpose of this work is to investigate the soundness and utility of a neural network-based approach as a framework for exploring the impact of image enhancement techniques on visual cortex activation. In a preliminary study, we prepare a set of state-of-the-art brain encoding models, selected among the top 10 methods that participated in The Algonauts Project 2023 Challenge [16]. We analyze their ability to make valid predictions about the effects of various image enhancement techniques on neural responses. Given the impossibility of acquiring the actual data due to the high costs associated with brain imaging procedures, our investigation builds up on a series of experiments. Specifically, we analyze the ability of brain encoders to estimate the cerebral reaction to various augmentations by evaluating the response to augmentations targeting objects (i.e., faces and words) with known impact on specific areas. Moreover, we study the predicted activation in response to objects unseen during training, exploring the impact of semantically out-of-distribution stimuli. We provide relevant evidence for the generalization ability of the models forming the proposed framework, which appears to be promising for the identification of the optimal visual augmentation filter for a given task, model-driven design strategies as well as for AR and VR applications.
Synthesis and Perceptual Scaling of High Resolution Naturalistic Images Using Stable Diffusion
Department of Psychology, Humboldt-Universität zu Berlin（柏林洪堡大学心理学系）；Bernstein Center for Computational Neuroscience Berlin and Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin（柏林伯恩斯坦计算神经科学中心和柏林先进神经成像中心，查理医学院）；Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany（马克斯·普朗克人类认知与脑科学研究所，莱比锡，德国）；Max Planck School of Cognition, Leipzig, Germany（马克斯·普朗克认知学校，莱比锡，德国）；Kavli Institute for Systems Neuroscience, Centre for Neural Computation, The Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, Jebsen Centre for Alzheimer’s Disease, Norwegian University of Science and Technology, Trondheim, Norway（凯斯琳系统神经科学研究所，神经计算中心，埃吉和保罗·布雷athen及弗雷德·卡维利微电路中心，朱贝森阿尔茨海默病中心，挪威科学与技术大学，特罗姆瑟，挪威）；Clinic of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany（柏林查理医学院神经科诊所）；Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, Berlin, Germany（柏林先进神经成像中心，查理医学院）；Research Cluster of Excellence “Science of Intelligence”, Technische（卓越研究集群“智能科学”，技术）
Shape2Animal: Creative Animal Generation from Natural Silhouettes
University of Science, Ho Chi Minh City, Vietnam（胡志明市科学技术大学）；Vietnam National University, Ho Chi Minh City, Vietnam（越南国家大学）；University of Dayton, Ohio, US（Dayton 大学）
SynMind: Reducing Semantic Hallucination in fMRI-Based Image Reconstruction
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学）；SketchX Lab, Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey（SketchX实验室，视觉、语音和信号处理中心（CVSSP）， Surrey大学）
Semantic segmentation of surgical hyperspectral images under geometric domain shifts
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；Heidelberg University Hospital（海德堡大学医院）
