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arXiv AI

AOI-Net: Structural Face AOI-Guided Eye-Gaze Track Representation Learning for Autism Spectrum Disorder Detection

AOI-Net introduces a structural face AOI-guided Eye‑Gaze Track Network that jointly models short‑term temporal dynamics and AOI‑level structural organization for Autism Spectrum Disorder detection. The network uses a gating mechanism to adaptively combine complementary representations and incorporates class‑distribution‑aware learning to address the imbalance between ASD and typically developing participants. Experiments on a large clinical eye‑tracking database with over 1,300 participants demonstrate that AOI‑Net outperforms state‑of‑the‑art methods and offers interpretable gaze‑behavior modeling for scalable AI‑driven ASD screening.

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arXiv Machine Learning
Oct 12

Bridging Human and Model Attention: Explainability Analysis of CNN, Mamba, and ViT Architectures with Gaze-Based Validation

The paper presents a quantitative study of how well the attention maps of CNNs, Vision Transformers, and Vision Mamba align with human visual perception, using eye‑tracking data as ground truth. A standardized normalization pipeline maps attribution maps and gaze heatmaps to spatial probability distributions, and alignment is measured with Jensen‑Shannon similarity, Chi‑square similarity, and Pearson correlation across fine‑grained classification, medical imaging, and emotion recognition tasks. Results show that CNNs exhibit more global, diffuse saliency that generally matches human perception, ViTs focus on fine‑grained, patch‑level features, and Vision Mamba performs well in medical tasks but diverges in natural images, with architectural and training choices affecting interpretability.

By Marco Stefani, Edoardo Parpaiola, Matteo Bino, Gianluca Caregnato, Francesco Roncolato, Daniel Fusaro, Loris Nanni, Alberto Pretto
llmssafety
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arXiv AI
Sep 28

UniAR: A Unified Framework for Autism Recognition Enhanced by Multi-View Prompt Learning

UniAR is a unified framework that improves autism spectrum disorder (ASD) recognition by using multi-granularity prompt learning and a large multimodal model to generate diagnostic descriptions at word, phrase, and sentence levels. It aligns these semantic representations with visual evidence through a Mixture-of-Experts-based Multi-Scale Alignment Module, enabling robust ASD detection across heterogeneous data types. Experiments on four brain MRI and facial expression benchmarks show that UniAR outperforms state‑of‑the‑art methods, achieving 75.9% accuracy on MRI and 91.6% on facial benchmarks, with gains of 1.5 and 1.2 percentage points respectively.

By Lei Xin, Zeheng Wang, Jiayin Zhu, Shihong Huang, Fanhu Zeng, Changjiang Jiang, Dengbo He, Yutao Yue, Zhenglun Kong
efficiencymultimodalbenchmarkssafety
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Hugging Face Trending Papers
Aug 11

Gaze Target Estimation Anywhere with Concepts

Estimating human gaze targets from images in-the-wild is an important and formidable task. Existing approaches primarily employ brittle, multi-stage pipelines that require explicit inputs, like head bounding boxes and human pose, in order to identify the subject of gaze analysis.

llmsbenchmarks
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arXiv Computer Vision
Sep 7

EyeMakeYou: Identity-, Task-, and Subjective-State-Conditioned Diffusion for High-Frequency Gaze Synthesis

EyeMakeYou is a multi‑conditional denoising diffusion model that synthesizes high‑frequency, subject‑specific gaze velocity sequences. It conditions on identity, task, and self‑reported subjective states (difficulty, mental tiredness, eye tiredness) to generate realistic 5‑second, 1000‑Hz bivariate gaze data from a reference trajectory. Experiments on the GazeBase dataset show that EyeMakeYou outperforms existing generative methods in spatial accuracy and real‑synthetic similarity while preserving task‑dependent associations with subjective reports.

By Kamrul Hasan, Mehedi Hasan Raju, Oleg V. Komogortsev
ragdiffusion
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arXiv Computer Vision
Sep 28

OpenVAM: Open-World Visual Attention Modeling with VLMs

OpenVAM is a new framework for visual attention modeling that combines a dense saliency map with language‑based explanations. It uses a decoupled design: a visual pathway for precise localization and a vision‑language head that generates grounded what/why explanations. The method is trained in three stages to preserve localization while adding language grounding, and a scalable pipeline creates multi‑domain annotations for evaluation.

By Kiana Hooshanfar, Amirhossein Kazerouni, Alireza Hosseini, Michael Brudno, Babak Taati
roboticssafety
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