Many visual phenomena suggest that humans use top-down generative or reconstructive processes to create visual percepts (e.g., imagery, object completion, pareidolia), but little is known about the role reconstruction plays in robust object recognition. We built an iterative encoder-decoder network that generates an object reconstruction and used it as top-down attentional feedback to route the most relevant spatial and feature information to feed-forward object recognition processes. We tested this model using the challenging out-of-distribution digit recognition dataset, MNIST-C, where 15 different types of transformation and corruption are applied to handwritten digit images. Our model showed strong generalization performance against various image perturbations, on average outperforming all other models including feedforward CNNs and adversarially trained networks. Our model is particularly robust to blur, noise, and occlusion corruptions, where shape perception plays an important role. Ablation studies further reveal two complementary roles of spatial and feature-based attention in robust object recognition, with the former largely consistent with spatial masking benefits in the attention literature (the reconstruction serves as a mask) and the latter mainly contributing to the model's inference speed (i.e., number of time steps to reach a certain confidence threshold) by reducing the space of possible object hypotheses. We also observed that the model sometimes hallucinates a non-existing pattern out of noise, leading to highly interpretable human-like errors. Our study shows that modeling reconstruction-based feedback endows AI systems with a powerful attention mechanism, which can help us understand the role of generating perception in human visual processing.
On the Utility of Foundation Models for Fast MRI: Vision-Language-Guided Image Reconstruction
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（麻省总医院Athinoula A. Martinos生物医学成像中心）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）
MPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstruction
1 Hawkes Institute, UCL \, 2 Dept. of Medical Physics；Biomedical Engineering, UCL 3 Dept. of Computer Science, UCL 4 Centre for AI, DS\&AI, AstraZeneca, UK
VGG-Tex: A Vivid Geometry-Guided Facial Texture Estimation Model for High Fidelity Monocular 3D Face Reconstruction
Renmin University of China（中国人民大学）；Tsinghua University（清华大学）；Beihang University（北京航空航天大学）；Beijing Academy of Blockchain and Edge Computing（北京区块链与边缘计算研究院）
DVG-Diffusion: Dual-View Guided Diffusion Model for CT Reconstruction from X-Rays
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）
Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction
University of Science and Technology of China（中国科学技术大学）；Institute of Advanced Technology（先进技术研究院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
LUCID: Learned Undersampling-Adaptive Consistency-Guided Inference with Deterministic Flow Matching for Sparse-View CT Reconstruction
School of Mathematical Sciences, Capital Normal University（首都师范大学数学科学学院）；National Center for Applied Mathematics Beijing, Capital Normal University（首都师范大学北京国家应用数学中心）；Academy for Multidisciplinary Studies, Capital Normal University（首都师范大学交叉科学研究院）
Undersampled MRI Reconstruction with Side Information-Guided Normalisation
The University of Queensland（昆士兰大学）；The Commonwealth Scientific and Industrial Research Organisation（联邦科学与工业研究组织）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Science and Technology of China（中国科学技术大学）
Toward High-Fidelity Visual Reconstruction: From EEG-Based Conditioned Generation to Joint-Modal Guided Rebuilding
Beijing University of Technology, Beijing（北京理工大学）；Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing（北京计算智能与智能系统重点实验室）
School of Software Technology, Zhejiang University（浙江大学软件学院）；Zhejiang Key Laboratory of Digital-Intelligence Service Technology, Zhejiang University（浙江大学数字智能服务技术重点实验室）；ZWSOFT Co., Ltd.（ZWSOFT公司）
Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion Probabilistic Model
Washington University in St. Louis（圣路易斯华盛顿大学）；Yale University（耶鲁大学）；Western University（西安大略大学）；Lawson Health Research Institute（劳森健康研究所）；McGill University（麦吉尔大学）；Siemens Healthineers AG（西门子医疗保健公司）；Mallinckrodt Institute of Radiology（马林克罗特放射学研究所）
Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising
School of Computer and Big Data, Minjiang University（闽江学院计算机与大数据学院）；School of Information, Renmin University of China（中国人民大学信息学院）；RMIT University（皇家墨尔本理工大学）；Victoria University（维多利亚大学）
DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume Reconstruction
Rutgers University（罗格斯大学）；FocusAI Inc.（FocusAI公司）；Walmart Global Tech（沃尔玛全球技术公司）；New York University（纽约大学）；Sichuan University West China Hospital（四川大学华西医院）
