ArticleScientific reports2021
COCO-Search18 fixation dataset for predicting goal-directed attention control.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Differential effects of free viewing and goal-directed search on visual attention: An eye-tracking study.PloS one · 2026Article
- Visual attention graph.Behavior research methods · 2025Article
- Integrating Bayesian and neural networks models for eye movement prediction in hybrid search.Scientific reports · 2025Article
- A simultaneous EEG and eye-tracking dataset for remote sensing object detection.Scientific data · 2025Article
- Look Hear: Gaze Prediction for Speech-directed Human Attention.Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision · 2025Article
- Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2024Article
- Oculomotor routines for perceptual judgments.Journal of vision · 2024Article
- Cues for predictive eye movements in naturalistic scenes.Journal of vision · 2023Article
- The neuroconnectionist research programme.Nature reviews. Neuroscience · 2023Review
- DeepGaze III: Modeling free-viewing human scanpaths with deep learning.Journal of vision · 2022Article
- Weighting the factors affecting attention guidance during free viewing and visual search: The unexpected role of object recognition uncertainty.Journal of vision · 2022Article
- Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning.Neurons, behavior, data analysis, and theory · 2021Article
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6 authors.
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Abstract
Attention control is a basic behavioral process that has been studied for decades. The currently best models of attention control are deep networks trained on free-viewing behavior to predict bottom-up attention control - saliency. We introduce COCO-Search18, the first dataset of laboratory-quality goal-directed behavior large enough to train deep-network models. We collected eye-movement behavior from 10 people searching for each of 18 target-object categories in 6202 natural-scene images, yielding [Formula: see text] 300,000 search fixations. We thoroughly characterize COCO-Search18, and benchmark it using three machine-learning methods: a ResNet50 object detector, a ResNet50 trained on fixation-density maps, and an inverse-reinforcement-learning model trained on behavioral search scanpaths. Models were also trained/tested on images transformed to approximate a foveated retina, a fundamental biological constraint. These models, each having a different reliance on behavioral training, collectively comprise the new state-of-the-art in predicting goal-directed search fixations. Our expectation is that future work using COCO-Search18 will far surpass these initial efforts, finding applications in domains ranging from human-computer interactive systems that can anticipate a person's intent and render assistance to the potentially early identification of attention-related clinical disorders (ADHD, PTSD, phobia) based on deviation from neurotypical fixation behavior.
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