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.
Boccignone al @cite_24 follow a similar path for automating saliency prediction in social interaction scenarios. They extract several priority maps, including (1) spatio-temporal saliency features using @cite_2 , (2) face maps from a convolutional neural network (CNN) face detector, and (3) the active speaker map from automatic lip sync algorithm of @cite_12 . Once the maps are extracted, a sampling scheme is employed to find attention attractors. Instead of relying on a complicated sampling scheme and multiple feature maps, we directly learn the mapping using a deep neural network.
@cite_24 @cite_12 @cite_2
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We address the deployment of perceptual attention to social interactions as displayed in conversational clips, when relying on multimodal information (audio and video). A probabilistic modelling framework is proposed that goes beyond the classic saliency paradigm while integrating multiple information cues. Attentional allocation is determined not just by stimulus-driven selection but, importantly, by social value as modulating the selection history of relevant multimodal items. Thus, the construction of attentional priority is the result of a sampling procedure conditioned on the potential value dynamics of socially relevant objects emerging moment to moment within the scene. Preliminary experiments on a publicly available dataset are presented. The goal of this work is to determine the audio-video synchronisation between mouth motion and speech in a video. We present a novel unified framework for both static and space -time saliency detection. Our method is a bottom-up approach and computes so-called local regression kernels (i.e., local descriptors) from the given image (or a video), which measure the likeness of a pixel (or voxel) to its surroundings. Visual saliency is then computed using the said “self-resemblance” measure. The framework results in a saliency map where each pixel (or voxel) indicates the statistical li kelihood of saliency of a feature matrix given its surrounding feature matrices. As a similarity measure, matrix cosine similarity (a generalization of cosine similarity) is employed. State of the art performance is demonstrated on commonly used human eye fixation data (static scenes [5] and dynamic scenes [16]) and some psychological patterns.