Spatial Attention

Latest papers

13 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Towards Scalable Context-Aware Single-Cell Spatial Transcriptomics Prediction from Histology Images
Sep 29, 2026Zijun Gao, Chunbin Gu, Jinxi Xiang +2Spatial Transcriptomics Spatial Attention
The Chinese University of Hong Kong · Stanford University School of Medicine
OpenVAM: Open-World Visual Attention Modeling with VLMs
Sep 25, 2026Kiana Hooshanfar, Amirhossein Kazerouni, Alireza Hosseini +2Visual Attention Gaze Estimation
University of Tehran · University of Toronto · Vector Institute +1
Adaptive Anisotropic Attention for Axis-Structured Signals
Sep 8, 2026Mahir Jain, Parshva Runwal, Aditya Ray Mishra +4EEG Decoding Attention Mechanisms
Mannas AI · IIT Mandi
TaskSense: Focusing on What Matters in World Models
Aug 6, 2026SM Mazharul Islam, Manfred HuberWorld Model Learning World Models
1The University of Texas at Arlington
Do We Really Need Transformers for Global Spatial Information Extraction in Traffic Forecasting?
Jul 14, 2026Qihang Zhang, Siyao Zhang, Letao Kang +3Intelligent Transportation Systems Traffic Flow Estimation
School of Transportation Science and Engineering, Beihang University, Beijing, China · Hangzhou International Innovation Institute, Beihang University, Hangzhou, China · Key Laboratory of Intelligent Transportation Technology and System (Ministry of Education), Beijing, China +2
Attention Alignment Between Humans and Vision-Language Models
Jun 16, 2026Isaac R. Christian, Udith Haputhanthrige, Hanna Hornfeld +4VLM Evaluation Visual Attention
Princeton Neuroscience Institute, Princeton University · Department of Psychology, Princeton University · Department of Computer Science, Princeton University +2
Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention
Jun 3, 2026Dhanesh RamachandramConcept Bottleneck Models Fine-Grained Image Classification
Vector Institute
Stereo Multistage Spatial Attention for Real-Time Mobile Manipulation Under Visual Scale Variation and Disturbances
May 1, 2026Xianbo Cai, Hideyuki Ichiwara, Hyogo Hiruma +3Mobile Manipulation Visuomotor Policy Learning
Department of Intermedia Art and Science, Waseda University, Tokyo, Japan · SB Intuitions Corp., Tokyo, Japan · Research and Development Group, Hitachi, Ltd., Ibaraki, Japan +1
SpatialMAGIC: A Hybrid Framework Integrating Graph Diffusion and Spatial Attention for Spatial Transcriptomics Imputation
Mar 6, 2026Sayeem Bin Zaman, Fahim Hafiz, Riasat AzimSpatial Attention Spatial Transcriptomics
Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh
Facial Expression Recognition Using Residual Masking Network
Mar 6, 2026Luan Pham, The Huynh Vu, Tuan Anh TranSpatial Attention Facial Expression Recognition
Research Department-Cinnamon AI, Viet Nam · Faculty of Computer Science & Engineering, Ho Chi Minh City-University of Technology (HCMUT), Viet Nam
Doubly Adaptive Channel and Spatial Attention for Semantic Image Communication by IoT Devices
Feb 26, 2026Soroosh Miri, Sepehr Abolhasani, Shahrokh Farahmand +2Deep Joint Source-Channel Coding Spatial Attention
School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran · Great Bay University, Dongguan 523000, China
Attention in Geometry: Scalable Spatial Modeling via Adaptive Density Fields and FAISS-Accelerated Kernels
Jan 5, 2026Zhaowen Fan, Yunxiang HanKernel Density Estimation Spatial Attention
School of Computer, SICHUAN UNIVERSITY, CHENGDU, CHINA, 610065
Few-Shot Specific Emitter Identification via Integrated Complex Variational Mode Decomposition and Spatial Attention Transfer
Dec 18, 2025Chenyu Zhu, Zeyang Li, Ziyi Xie +1Few-Shot Learning Spatial Attention
School of Information Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China · School of Electrical Engineering and Computer Science, and the Digital Futures, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden · Ranplan Wireless Network Design Ltd., Cambridge, CB23 3UY, U.K.

Sep 29, 2026J/K move · Enter open · S save

Towards Scalable Context-Aware Single-Cell Spatial Transcriptomics Prediction from Histology Images

Zijun Gao, Chunbin Gu, Jinxi Xiang, Xiangde Luo+1

The Chinese University of Hong Kong · Stanford University School of Medicine

Spatial Transcriptomics Spatial Attention Spatial Gene Expression Prediction Gene Expression Prediction from Histopathology Images Whole-Slide Image Analysis

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Predicting gene expression from H&E-stained histology images offers a scalable alternative to costly spatial transcriptomics, yet most existing methods operate at the spot level, where signals from multiple cells are aggregated and critical cellular heterogeneity is obscured. Extending this paradigm to single-cell resolution is non-trivial. Naively applying pathology foundation models faces a scale mismatch: their patch-level representations mix multiple cells, whereas per-cell cropping or resizing distorts morphology and removes local context. Conversely, segmentation-based models without strong pretrained visual encoders often lack the morphological representation capacity needed for accurate molecular prediction and inherit errors from imperfect cell boundary masks. Here, we present CELLO, an efficient end-to-end framework that performs a single pathology foundation model forward pass per image and uses grid sampling to extract location-specific features for all cells simultaneously. We further introduce a distance-decay cross-attention module that refines each cell representation using spatially biased local morphological context. Using 52 public Xenium-H&E pairs from HEST-1k that span 12 organs and approximately 10 million cells, CELLO improves the average predictive accuracy over the evaluated baselines while reducing the mean whole-slide inference time compared to DeepSpot2Cell, a 14.0x speed-up on average that excludes upstream cell segmentation. Our work establishes a scalable foundation for single-cell gene expression prediction from H&E images.