SID4VAM: A Benchmark Dataset With Synthetic Images for Visual Attention Modeling

Berga, David; Vidal, Xose Ramon Fernandez; Otazu, Xavier; Pardo, Xosé M.

doi:10.1109/iccv.2019.00888

Cited by 9 publications

(13 citation statements)

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“…On behalf of model biological plasusibility on 72 V1 function and its computations, we present a unified model of lateral connections in V1, able to predict attention 73 (both in free-viewing and visual search) from real and synthetic color images while mimicking physiological properties 74 of the neural circuitry stated previously. 75 76 77 The HVS perceives the light at distinct wavelengths of the visual spectrum and separates them to distinct channels 78 for further processing in the cortex. First, retinal photoreceptors (or RP, corresponding to rod and cone cells) are 79 photosensitive to luminance (rhodopsin-pigmented) and color (photopsin-pigmented) [41,42].…”

Section: Introductionmentioning

confidence: 99%

“…'s work [7], the IoR can be applied to static saliency models by substracting the accumulated inhibitory map to the 256 saliency map during each gaze (̂ − { } ). ) and 230 psychophysical images (SID4VAM [17,75]). Generically, experimentation for these type 262 of datasets [76] capture fixations from about 5 to 55 subjects, looking at a monitor inside a luminance controlled 263 room while being restrained with a chin rest, located at a relative distance of 30-40 pixels per degree of visual angle 264 ( ).…”

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confidence: 99%

“…In order to evaluate performance on visual search, we utilised two metrics that account for the 296 ground truth mask of specific regions for search and the saliency map (in this context, it could be considered as a 297 "relevance" map) or predicted saccade coordinates (from locations with highest neuronal activity). The Saliency Index 298 (SI) [17,75,79] calculates the amount of energy of a saliency map inside a ROI ( ) with respect to the one outside 299 ( ), calculated as: = ( − )∕ . For the case of saccades in visual search, we considered to calculate the 300 probability of fixations inside the ROI (PFI).…”

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confidence: 99%

“…In the next section we show how our model can 400 preproduce eye movements beyond free-viewing tasks by modulating of inhibitory top-down signals. 402 We have compared our model predictions with bottom-up only (NSWAM | NSWAM-CM) and with top-down 403 inhibitory modulation (NSWAM+VS | NSWAM-CM+VS) for singleton search stimuli (for both real [10] and synthetic 404 targets [75]). Top-down selection is applied to our low-level feature dimensions (scale, orientation, channel opponency 405 and its polarity).…”

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confidence: 99%

“…Performance on visual search evaluated on each distinct low-level feature, stimulus instances are from SID4VAM's dataset[17,75].…”

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confidence: 99%

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Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1

Berga

Otazu

2019

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Previous studies in that line suggested that lateral interactions of V1 cells are responsible, among other visual effects, of bottom-up visual attention (alternatively named visual salience or saliency). Our objective is to mimic these connections in the visual system with a neurodynamic network of firing-rate neurons. Early subcortical processes (i.e. retinal and thalamic) are functionally simulated. An implementation of the cortical magnification function is included to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search conditions. Results show that our model outpeforms other biologically-inpired models of saliency prediction as well as to predict visual saccade sequences during free viewing. We also show how temporal and spatial characteristics of inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) predict attention at distinct image contexts. Author summarySaliency maps are the representations of how certain visual regions attract attention in a visual scene, and these can be measured with eye movements. A myriad of computational models with artificial and biological inspiration have been able to acquire outstanding predictions of human fixations. However, most of these models have been built specifically for visual saliency, a characteristic that denies their biological plausibility for modeling distinct visual processing mechanisms or other visual processes simultaneously. In addition to saliency, our approach is also able to efficiently work for other tasks (without applying any type of training or optimization and keeping the same parametrization) such as Visual Search, Visual Discomfort [1], Brightness [2] and Color Induction [3]. By performing simulations of human physiology and its mechanisms, we propose to build a unified model that could be extended to predict and understand distinct perceptual processes in which V1 is responsible.2 discriminate redundant information [4][5][6]. In order to filter or select the information to 3 May 3, 2019 1/32 be processed in higher areas of visual processing in the brain, the HVS guides eye 4 movements towards regions that appear to be visually conspicuous or distinct in the 5 scene. This phenomena was observed during visual search tasks [7, 8], where detecting 6 early visual features (such as orientation, color or size) was done in parallel 7 (pre-attentively) or required either a serial "binding" step depending on scene context. 8 Koch & Ullman [9] came up with the hypothesis that neuronal mechanisms involved in 9 selective visual attention generate a unique "master" map from visual scenes, coined 10 with the term "saliency map". From that, Itti, Koch & Ullman [10] presented a 11 computational i...

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