This paper presents a conceptually simple and effective Deep Audio-Visual Eembedding for dynamic saliency prediction dubbed DAVE". Several behavioral studies have shown a strong relation between auditory and visual cues for guiding gaze during scene free viewing. The existing video saliency models, however, only consider visual cues for predicting saliency over videos and neglect the auditory information that is ubiquitous in dynamic scenes. We propose a multimodal saliency model that utilizes audio and visual information for predicting saliency in videos. Our model consists of a two-stream encoder and a decoder. First, auditory and visual information are mapped into a feature space using 3D Convolutional Neural Networks (3D CNNs). Then, a decoder combines the features and maps them to a final saliency map. To train such model, data from various eye tracking datasets containing video and audio are pulled together. We further categorised videos into social', nature', and miscellaneous' classes to analyze the models over different content types. Several analyses show that our audio-visual model outperforms video-based models significantly over all scores; overall and over individual categories. Contextual analysis of the model performance over the location of sound source reveals that the audio-visual model behaves similar to humans in attending to the location of sound source. Our endeavour demonstrates that audio is an important signal that can boost video saliency prediction and help getting closer to human performance.
Parallel to studies on auditory attention, numerous works have explored visual attention. The span of behavioral studies on visual attention is broad and covers a wide range of experiments on primates and humans. In such experiments, an observer is often presented with a stimulus and his neural and or behavioral responses are recorded ( using single unit recording, brain imaging, or eye tracking) @cite_40 .
@cite_40
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According to conventional neurobiological accounts of visual attention, attention serves to enhance extrastriate neuronal responses to a stimulus at one spatial location in the visual field. However, recent results from recordings in extrastriate cortex of monkeys suggest that any enhancing effect of attention is best understood in the context of competitive interactions among neurons representing all of the stimuli present in the visual field. These interactions can be biased in favour of behaviourally relevant stimuli as a result of many different processes, both spatial and non–spatial, and both bottom–up and top–down. The resolution of this competition results in the suppression of the neuronal representations of behaviourally irrelevant stimuli in extrastriate cortex. A main source of top–down influence may derive from neuronal systems underlying working memory. Visual attention poses a mechanism for the selection of behaviourally relevant information from natural scenes which usually contain multiple objects. The aim of the present work is to formulate a neurodynamical model of selective visual attention based on the "biased competition hypothesis" and structured in several network modules which can be related with the different areas of the dorsal and ventral path of the visual cortex. Spatial and object attention are accomplished by a multiplicative gain control that emerges dynamically through intercortical mutual biased coupling. We also include in our computational model the "resolution hypothesis" in order to explain the role of the neurodynamics control of spatial resolution evidenced in psychophysical experiments. We propose that V1 neurons have different latencies depending on the spatial frequency to which they respond more sensitively. In concrete, we pose that V1 neurons sensitive to low spatial frequency are faster than V1 neurons sensitive to high spatial frequency. In this sense, a scene is first predominantly analysed at a coarse resolution level and the dynamics enhances subsequently the resolution at the location of an object until the object is identified. Attention can be focused volitionally by “top-down” signals derived from task demands and automatically by “bottom-up” signals from salient stimuli. The frontal and pariet al cortices are involved, but their neural activity has not been directly compared. Therefore, we recorded from them simultaneously in monkeys. Prefrontal neurons reflected the target location first during top-down attention, whereas pariet al neurons signaled it earlier during bottom-up attention. Synchrony between frontal and pariet al areas was stronger in lower frequencies during top-down attention and in higher frequencies during bottom-up attention. This result indicates that top-down and bottom-up signals arise from the frontal and sensory cortex, respectively, and different modes of attention may emphasize synchrony at different frequencies. Abstract In 2 experiments, the development of auditory selective attention in children was assessed. The participants, aged 5, 7, and 9 years, responded to target stimuli in the left ear for 1 series (ignoring the standard stimuli) and in the right ear for the other series. In Experiment 1, event-related potentials (ERPs) evoked by the auditory stimuli were recorded from frontal, central, and pariet al sites. The 9-year-olds showed a greater processing negativity (Nd) to the attended channel compared with the 5-year-olds. Both 7-and 9-year-olds showed significantly larger amplitudes for the P3 component of ERPs in the attended vs. ignored condition. Behaviorally, the 5-year-olds made fewer hits and more false alarms than did older children, and the 9-year-olds made significantly more false alarms than did the 7-year-olds. The results of Experiment 2 showed that the detriment in the performance of the 9-year-olds was a result of task parameters. The inability of 5-year-olds to attend selectively appears to ...