State-of-the-Art in Visual Attention Modeling

By Ali Borji, Laurent Itti. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013

1,931 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/aa57b9bdb0736cbb38abf0a9d12047212c2a2425

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

In Human-Computer Interaction

Citations in the corpus

In the Eye of the Beholder: Gaze and Actions in First Person Video (2020, background): “Gaze estimation is well studied in computer vision [83].”
Learning Race from Face: A Survey (2014, background): “A promising direction for future research, therefore, is the development of recognition models that take visual attention models [236], [237], [238] and eye tracking data [232], [234] into account.”
TVSum: Summarizing web videos using titles (2015, background): “Much work has been devoted to unsupervised video summarization, assessing the importance of frames using visual attention [16, 5], interestingness [21, 20], user engagement [33, 35], content frequency [52] and nonredundancy [30, 50].”
Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground (2018, methodology): “tention-grabbing objects in a scene and then extract pixel-accurate silhouettes of the objects. The merit of SOD lies in it applications in many other computer vision tasks including: visual tracking [4], image retrieval [14,16], computer graphics [9], content aware image resizing [45], and weakly s”
Seeing with Humans: Gaze-Assisted Neural Image Captioning (2016, background): “There has been remarkable progress in learning-based visual saliency models that demonstrated good performance for predicting where humans look in a purely bottom-up manner [20].”
What Do Different Evaluation Metrics Tell Us About Saliency Models? (2016, background): “Saliency is often motivated as a useful representation for image processing applications such as image re-targeting, compression, and transmission, object and motion detection, and image retrieval and matching [6], [38].”
Deeply Supervised Salient Object Detection with Short Connections (2016, methodology): “Taking this fact into consideration, various hand-crafted features have been designed, employing either global or local cues (See [23], [24] for reviews).”
Learning to Predict Gaze in Egocentric Video (2013, methodology): “Several methods also model saliency in a probabilistic framework, where the rarity of the feature defines its saliency [2].”
A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping (2019, methodology): “We refer readers to two recent surveys [27], [28] for more details of early attention models.”
Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention (2020, background): “Predicting what people visually attend to in images (saliency prediction) is a long-standing challenge in neuroscience and computer vision [4, 6, 40].”
A Stagewise Refinement Model for Detecting Salient Objects in Images (2017, background): “, [21, 20, 39, 5, 6], which are vital in understanding scenes (e.”
Attentive Systems: A Survey (2017, background): “The recent progresses of state-of-the-art works are extensively reviewed in Borji and Itti (2013), Borji et al. (2012, 2015), and Li et al. (2014).”
Salient Object Detection: A Benchmark (2015, background): “V ISUAL attention, the astonishing capability of human visual system to selectively process only the salient visual stimuli in details, has been investigated by multiple disciplines such as cognitive psychology, neuroscience, and computer vision [2]–[5].”
Global Contrast Based Salient Region Detection (2015, background): “In contrast, computationally identifying such salient object regions [2], [3], that match the human annotators’ behaviour when they have been asked to pick a salient object in an image, is very challenging.”
Saliency Detection: A Boolean Map Approach (2013, background): “Computing such saliency maps has recently raised a great amount of research interest (see [4] for a review) and has been shown to be beneficial in many applications, e.”
Revisiting Video Saliency Prediction in the Deep Learning Era (2021, methodology): “There are several ways to measure the agreement between model predictions and human eye movements [29], [52].”
In the Eye of Beholder: Joint Learning of Gaze and Actions in First Person Video (2018, background): “Gaze estimation is well studied in computer vision [2].”
Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study (2013, background): “For more details on evaluation scores please refer to [24]2.”
Active Object Localization with Deep Reinforcement Learning (2015, background): “Visual attention models have been investigated with the goal of predicting where an observer is likely to orient the gaze (see [3] for a recent survey).”