Top-down attention allows people to focus on task-relevant visual information. Is the resulting perceptual boost task-dependent in naturalistic settings? We aim to answer this with a large-scale computational experiment. First, we design a collection of visual tasks, each consisting of classifying images from a chosen task set (subset of ImageNet categories). The nature of a task is determined by which categories are included in the task set. Second, on each task we train an attention-augmented neural network and then compare its accuracy to that of a baseline network. We show that the perceptual boost of attention is stronger with increasing task-set difficulty, weaker with increasing task-set size and weaker with increasing perceptual similarity within a task set.
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）
Perceived face similarity reflects inverse-generative and naturalistic discriminative objectives
Department of Psychology（心理学系）；Department of Behavioural and Cognitive Sciences（行为与认知科学系）；MRC Cognition and Brain Sciences Unit（认知与脑科学单位）；School of Psychology（心理学系）；Department of Neuroscience（神经科学系）；Department of Industrial Engineering and Management（工业工程与管理系）；School of Brain Sciences and Cognition（脑科学与认知系）
EEG2Vision: A Multimodal EEG-Based Framework for 2D Visual Reconstruction in Cognitive Neuroscience
Università Politecnica delle Marche（马尔凯理工大学）；University of Macerata（马切拉塔大学）；Horizon Intelligence Labs（地平线智能实验室）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）
University of Oxford（牛津大学）；University College London（伦敦大学学院）
Top-down attention allows neural networks, both artificial and biological, to focus on the information most relevant for a given task. This is known to enhance performance in visual perception. But it remains unclear how attention brings about its perceptual boost, especially when it comes to naturalistic settings like recognising an object in an everyday scene. What aspects of a visual task does attention help to deal with? We aim to answer this with a computational experiment based on a general framework called task-oriented ablation design. First we define a broad range of visual tasks and …
Northwestern University（西北大学）；University of Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）
Computed tomography is widely used to examine internal structures in a non-destructive manner. To obtain high-quality reconstructions, one typically has to acquire a densely sampled trajectory to avoid angular undersampling. However, many scenarios require a sparse-view measurement leading to streak-artifacts if unaccounted for. Current methods do not make full use of the domain-specific information, and hence fail to provide reliable reconstructions for highly undersampled data. We present a novel framework for sparse-view tomography by decoupling the reconstruction into two steps: First, we …
Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality Assessment
School of Software, Dalian University of Technology（大连理工大学软件学院）；International School of Information Science and Engineering, Dalian University of Technology（大连理工大学国际信息与工程学院）
Heriot-Watt University（赫瑞-瓦特大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Carnegie Mellon University（卡内基梅隆大学）
In visual guessing games, a Guesser has to identify a target object in a scene by asking questions to an Oracle. An effective strategy for the players is to learn conceptual representations of objects that are both discriminative and expressive enough to ask questions and guess correctly. However, as shown by Suglia et al. (2020), existing models fail to learn truly multi-modal representations, relying instead on gold category labels for objects in the scene both at training and inference time. This provides an unnatural performance advantage when categories at inference time match those at tr…
Perceptual Depth Quality Assessment of Stereoscopic Omnidirectional Images
School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）；Department of Electrical and Computer Engineering, University of Waterloo（滑铁卢大学电气与计算机工程系）
Chinese Academy of Sciences（中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Walton Institute for Information and Communication Systems Science（沃尔顿信息与通信系统科学研究所）；Waterford Institute of Technology（沃特福德理工学院）；University of Johannesburg（约翰内斯堡大学）；University of Sindh（信德大学）；Sungkyunkwan University（成均馆大学）
Deep learning based single image super resolution (SISR) algorithms has revolutionized the overall diagnosis framework by continually improving the architectural components and training strategies associated with convolutional neural networks (CNN) on low-resolution images. However, existing work lacks in two ways: i) the SR output produced exhibits poor texture details, and often produce blurred edges, ii) most of the models have been developed for a single modality, hence, require modification to adapt to a new one. This work addresses (i) by proposing generative adversarial network (GAN) wi…
Recent progress in deep learning-based models has improved photo-realistic (or perceptual) single-image super-resolution significantly. However, despite their powerful performance, many methods are difficult to apply to real-world applications because of the heavy computational requirements. To facilitate the use of a deep model under such demands, we focus on keeping the network efficient while maintaining its performance. In detail, we design an architecture that implements a cascading mechanism on a residual network to boost the performance with limited resources via multi-level feature fus…
Using Multimodal Deep Neural Networks to Disentangle Language from Visual Aesthetics
Harvard University（哈佛大学）；Harvard School of Public Health（哈佛公共卫生学院）；Massachusetts General Hospital（麻省总医院）；Massachusetts Institute of Technology（麻省理工学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
