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.
Summary Our nervous system is confronted with a barrage of sensory stimuli, but neural resources are limited and not all stimuli can be processed to the same extent. Mechanisms exist to bias attention toward the particularly salient events, thereby providing a weighted representation of our environment [1]. Our understanding of these mechanisms is still limited, but theoretical models can replicate such a weighting of sensory inputs and provide a basis for understanding the underlying principles [2, 3]. Here, we describe such a model for the auditory system—an auditory saliency map. We experimentally validate the model on natural acoustical scenarios, demonstrating that it reproduces human judgments of auditory saliency and predicts the detectability of salient sounds embedded in noisy backgrounds. In addition, it also predicts the natural orienting behavior of naive macaque monkeys to the same salient stimuli. The structure of the suggested model is identical to that of successfully used visual saliency maps. Hence, we conclude that saliency is determined either by implementing similar mechanisms in different unisensory pathways or by the same mechanism in multisensory areas. In any case, our results demonstrate that different primate sensory systems rely on common principles for extracting relevant sensory events. Most models of visual search, whether involving overt eye movements or covert shifts of attention, are based on the concept of a saliency map, that is, an explicit two-dimensional map that encodes the saliency or conspicuity of objects in the visual environment. Competition among neurons in this map gives rise to a single winning location that corresponds to the next attended target. Inhibiting this location automatically allows the system to attend to the next most salient location. We describe a detailed computer implementation of such a scheme, focusing on the problem of combining information across modalities, here orientation, intensity and color information, in a purely stimulus-driven manner. The model is applied to common psychophysical stimuli as well as to a very demanding visual search task. Its successful performance is used to address the extent to which the primate visual system carries out visual search via one or more such saliency maps and how this can be tested.