Humans process visual scenes selectively and sequentially using attention. Central to models of human visual attention is the saliency map. We propose a hierarchical visual architecture that operates on a saliency map and uses a novel attention mechanism to sequentially focus on salient regions and take additional glimpses within those regions. The architecture is motivated by human visual attention, and is used for multi-label image classification on a novel multiset task, demonstrating that it achieves high precision and recall while localizing objects with its attention. Unlike conventional multi-label image classification models, the model supports multiset prediction due to a reinforcement-learning based training process that allows for arbitrary label permutation and multiple instances per label.
Visual attention can be classified into and components. Covert attention precedes eye movements, and is intuitively used to monitor the environment and guide eye movements to salient regions @cite_33 @cite_9 . Two particular functions of covert attention motivate the Gaussian attention mechanism proposed below: noise exclusion, which modifies perceptual filters to enhance the signal portion of the stimulus and mitigate the noise; and distractor suppression, which refers to suppressing the representation strength outside an attention area @cite_33 . Further inspiring the proposed attention mechanism is evidence from cueing @cite_8 , multiple object tracking @cite_45 , and fMRI @cite_29 studies, which indicate that covert attention can be deployed to , regions that and can be conceptually viewed as multiple "spotlights".
@cite_33 @cite_8 @cite_29 @cite_9 @cite_45
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This review focuses on covert attention and how it alters early vision. I explain why attention is considered a selective process, the constructs of covert attention, spatial endogenous and exogenous attention, and feature-based attention. I explain how in the last 25 years research on attention has characterized the effects of covert attention on spatial filters and how attention influences the selection of stimuli of interest. This review includes the effects of spatial attention on discriminability and appearance in tasks mediated by contrast sensitivity and spatial resolution; the effects of feature-based attention on basic visual processes, and a comparison of the effects of spatial and feature-based attention. The emphasis of this review is on psychophysical studies, but relevant electrophysiological and neuroimaging studies and models regarding how and where neuronal responses are modulated are also discussed. A partial report procedure was used to test the ability of observers to split attention over noncontiguous locations. Observers reported the identity of 2 targets that appeared within a 5 X 5 stimulus array, and cues (validity = 80 ) informed them of the 2 most likely target locations. On invalid trials, 1 of the targets appeared directly in between the cued locations. Experiments 1, la, and 2 showed a strong accuracy advantage at cued locations compared with intervening ones. This effect was larger when the cues were arranged horizontally rather than vertically. Experiment 3 suggests that this effect of cue orientation reflects an advantage for processing targets that appear in different hemifields. Experiments 4 and 4a suggest that the primary mechanism supporting the flexible deployment of spatial attention is the suppression of interference from stimuli at unattended locations. There is a substantial body of research showing that when observers direct attention to specific parts of the visual field, information processing is facilitated at attended locations relative to unattended locations. A basic question is how flexibly attention can be deployed over space. Most models of spatial attention have assumed that attention is allocated over contiguous regions of space (e.g., Eriksen & Yeh, 1985; Posner, Snyder, & Davidson, 1980), and various empirical tests have supported this claim (e.g., Eriksen & Yeh, 1985; Heinze, Luck, Muente, Goes, Mangun, & Hillyard, 1994; McCormick & Klein, 1990; Pan & Eriksen, 1993; Posner, Snyder, & Davidson, 1980). However, more recent evidence has suggested that allocation of attention to noncontiguous regions is possible. Kramer and Hahn (1995) and Hahn and Kramer (1998) studied the influence of distractor compatibility at locations directly in between two cued locations and found that under certain conditions performance was unaffected by the distractors. In particular, they noted that when the distractors were presented by removing selected parts of a premask (the nononset condition), observers were able to suppress their influence. However, when the intervening distractors were presented as sudden-onset stimuli, distractor compatibility effects emerged. The investigators concluded that observers could divide attention effectively between noncontiguous locations but that this split focus was disrupted when new perceptual objects appeared in the intervening locations. They used a very strict criterion for evidence of split attention, however—namely, that distractors have absolutely no effect on target processing. The fact Visual attention is a mechanism which filters out redundant visual information and detects the most relevant parts of our visual field. Automatic determination of the most visually relevant areas would be useful in many applications such as image and video coding, watermarking, video browsing, and quality assessment. Many research groups are currently investigating computational modeling of the visual attention system. The first published computational models have been based on some basic and well-understood human visual system (HVS) properties. These models feature a single perceptual layer that simulates only one aspect of the visual system. More recent models integrate complex features of the HVS and simulate hierarchical perceptual representation of the visual input. The bottom-up mechanism is the most occurring feature found in modern models. This mechanism refers to involuntary attention (i.e., salient spatial visual features that effortlessly or involuntary attract our attention). This paper presents a coherent computational approach to the modeling of the bottom-up visual attention. This model is mainly based on the current understanding of the HVS behavior. Contrast sensitivity functions, perceptual decomposition, visual masking, and center-surround interactions are some of the features implemented in this model. The performances of this algorithm are assessed by using natural images and experimental measurements from an eye-tracking system. Two adequate well-known metrics (correlation coefficient and Kullbacl-Leibler divergence) are used to validate this model. A further metric is also defined. The results from this model are finally compared to those from a reference bottom-up model. Visual attention is the cognitive process of directing our gaze on one aspect of the visual field while ignoring others. The mainstream approach to modeling focal visual attention involves identifying saliencies in the image and applying a search process to the salient regions. However, such inference schemes commonly fail to accurately capture perceptual attractors, require massive computational effort and, generally speaking, are not biologically plausible. This paper introduces a novel approach to the problem of visual search by framing it as an adaptive learning process. In particular, we devise an approximate optimal control framework, based on reinforcement learning, for actively searching a visual field. We apply the method to the problem of face detection and demonstrate that the technique is both accurate and scalable. Moreover, the foundations proposed here pave the way for extending the approach to other large-scale visual perception problems.