Papers Scanpath prediction
“Scanpath prediction” 태그가 달린 논문 43편 · 필터 해제
DeepGaze3.5-VL: Modeling Scanpaths via Autoregressive Token Prediction
Understanding human visual attention on a scene over time has applications in domains such as interface design and inferring cognitive states. Modeling visual scanpaths has historically relied on specialized architecture…
Scanpath predictionFast Human Attention Prediction for Fixation-guided Active Perception in Autonomous Navigation
Human visual attention relies on structured scanpaths to efficiently process scenes, yet instilling this behavior into robot autonomy is in its infancy and hindered by the high,computational costs of existing predictive …
Reinforcement LearningScanpath predictionEyettention II: A Dual-Sequence Architecture for Modeling Fixation Location, Within-Word Landing Position, and Fixation Duration in Reading
The way our eyes move while reading provides valuable insights into both the reader's cognitive processes and the properties of the text. In particular, eye-tracking-while-reading data has shown to be highly beneficial i…
Scanpath predictionObject Referring-Guided Scanpath Prediction with Perception-Enhanced Vision-Language Models
Object Referring-guided Scanpath Prediction (ORSP) aims to predict the human attention scanpath when they search for a specific target object in a visual scene according to a linguistic description describing the object.…
Referring ExpressionScanpath predictionCost-Efficient Multi-Scale Fovea for Semantic-Based Visual Search Attention
Semantics are one of the primary sources of top-down preattentive information. Modern deep object detectors excel at extracting such valuable semantic cues from complex visual scenes. However, the size of the visual inpu…
Scanpath predictionObject DetectionMeasuring and Predicting Where and When Pathologists Focus their Visual Attention while Grading Whole Slide Images of Cancer
The ability to predict the attention of expert pathologists could lead to decision support systems for better pathology training. We developed methods to predict the spatio-temporal (where and when) movements of patholog…
Scanpath predictionModeling Human Gaze Behavior with Diffusion Models for Unified Scanpath Prediction
Predicting human gaze scanpaths is crucial for understanding visual attention, with applications in human-computer interaction, autonomous systems, and cognitive robotics. While deep learning models have advanced scanpat…
Scanpath predictionHuman Scanpath Prediction in Target-Present Visual Search with Semantic-Foveal Bayesian Attention
In goal-directed visual tasks, human perception is guided by both top-down and bottom-up cues. At the same time, foveal vision plays a crucial role in directing attention efficiently. Modern research on bio-inspired comp…
Scanpath predictionObject DetectionCT-ScanGaze: A Dataset and Baselines for 3D Volumetric Scanpath Modeling
Understanding radiologists' eye movement during Computed Tomography (CT) reading is crucial for developing effective interpretable computer-aided diagnosis systems. However, CT research in this area has been limited by t…
Scanpath predictionArtificially Generated Visual Scanpath Improves Multi-label Thoracic Disease Classification in Chest X-Ray Images
Expert radiologists visually scan Chest X-Ray (CXR) images, sequentially fixating on anatomical structures to perform disease diagnosis. An automatic multi-label classifier of diseases in CXR images can benefit by incorp…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONScanpath predictionThoracic Disease ClassificationFew-shot Personalized Scanpath Prediction
A personalized model for scanpath prediction provides insights into the visual preferences and attention patterns of individual subjects. However, existing methods for training scanpath prediction models are data-int…
PredictionScanpath predictionCaption-Driven Explorations: Aligning Image and Text Embeddings through Human-Inspired Foveated Vision
Understanding human attention is crucial for vision science and AI. While many models exist for free-viewing, less is known about task-driven image exploration. To address this, we introduce CapMIT1003, a dataset with ca…
Scanpath predictionGazeXplain: Learning to Predict Natural Language Explanations of Visual Scanpaths
While exploring visual scenes, humans' scanpaths are driven by their underlying attention processes. Understanding visual scanpaths is essential for various applications. Traditional scanpath models predict the where and…
DecoderPredictionScanpath predictionLook Hear: Gaze Prediction for Speech-directed Human Attention
For computer systems to effectively interact with humans using spoken language, they need to understand how the words being generated affect the users' moment-by-moment attention. Our study focuses on the incremental pre…
DecoderGaze PredictionReferring ExpressionScanpath predictionOAT: Object-Level Attention Transformer for Gaze Scanpath Prediction
Visual search is important in our daily life. The efficient allocation of visual attention is critical to effectively complete visual search tasks. Prior research has predominantly modelled the spatial allocation of visu…
DecoderObjectPredictionScanpath predictionPathformer3D: A 3D Scanpath Transformer for 360° Images
Scanpath prediction in 360{\deg} images can help realize rapid rendering and better user interaction in Virtual/Augmented Reality applications. However, existing scanpath prediction models for 360{\deg} images execute sc…
DecoderPredictionScanpath predictionPredicting Visual Attention in Graphic Design Documents
We present a model for predicting visual attention during the free viewing of graphic design documents. While existing works on this topic have aimed at predicting static saliency of graphic designs, our work is the firs…
Scanpath predictionMultimodal Learning and Cognitive Processes in Radiology: MedGaze for Chest X-ray Scanpath Prediction
Predicting human gaze behavior within computer vision is integral for developing interactive systems that can anticipate user attention, address fundamental questions in cognitive science, and hold implications for field…
Scanpath predictionUnified Dynamic Scanpath Predictors Outperform Individually Trained Neural Models
Previous research on scanpath prediction has mainly focused on group models, disregarding the fact that the scanpaths and attentional behaviors of individuals are diverse. The disregard of these differences is especially…
Saliency PredictionScanpath predictionBeyond Average: Individualized Visual Scanpath Prediction
Understanding how attention varies across individuals has significant scientific and societal impacts. However, existing visual scanpath models treat attention uniformly, neglecting individual differences. To bridge this…
PredictionScanpath prediction