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# Multisensory 3D Saliency for Artificial Attention Systems

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In this paper we present proof-of-concept for a novel solution consisting of a short-term 3D memory for artificial attention systems, loosely inspired in perceptual processes believed to be implemented in the human brain. Our solution supports the implementation of multisen-sory perception and stimulus-driven processes of attention. For this purpose , it provides (1) knowledge persistence with temporal coherence tackling potential salient regions outside the field of view, via a panoramic, log-spherical inference grid; (2) prediction, by using estimates of local 3D velocity to anticipate the effect of scene dynamics; (3) spatial correspondence between volumetric cells potentially occupied by proto-objects and their corresponding multisensory saliency scores. Visual and auditory signals are processed to extract features that are then filtered by a proto-object segmentation module that employs colour and depth as discriminatory traits. We consider as features, apart from the commonly used colour and intensity contrast, colour bias, the presence of faces, scene dynamics and also loud auditory sources. Combining conspicuity maps derived from these features we obtain a 2D saliency map, which is then processed using the probability of occupancy in the scene to construct the final 3D saliency map as an additional layer of the Bayesian Volumetric Map (BVM) inference grid.
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## Supplementary resources (2)

... This requires the implementation of the representational skill, which toddlers achieve in early stages of development [12], and involves discovering the object that the interlocutor is referring to. One of the possible approaches discussed in the literature is to provide an internal representation of the environment that works as a short-term memory and stores important information for attention [4,8], and then use the gaze cue to modulate the potential attended objects. ...
... Regarding to attention, the information provided by the different sensors must be subjected to a spatial correspondence [3]. For instance, a sound source should be related with its potential origin location in order to make possible the correspondent attention action [8]. In fact, in overt attention, where the scene is partially observed and changes depending on the actions (e.g., head movements), if we do not enable the machine to have an internal representation of the environment with temporal registration, its actions will become reactive for each location and angles setup. ...
... In this work we will use the 3D log-spherical representation proposed in [4] to codify the perceived environment. The method is also valid when the saliency of the scene is modelled [8]. ...
Chapter
Human gaze is one of the most important cue for social robotics due to its embedded intention information. Discovering the location or the object that an interlocutor is staring at, gives the machine some insight to perform the correct attentional behaviour. This work presents a fast voxel traversal algorithm for estimating the potential locations that a human is gazing. Given a 3D occupancy map in log-spherical coordinates and the gaze vector, we evaluate the regions that are relevant for attention by computing the set of intersected voxels between an arbitrary gaze ray in the 3D space and a log-spherical bounded section defined by $$\rho \in (\rho _{min},\rho _{max}),\theta \in (\theta _{min},\theta _{max} ),\phi \in (\phi _{min},\phi _{max})$$. The first intersected voxel is computed in closed form and the rest are obtained by binary search guaranteeing no repetitions in the intersected set. The proposed method is motivated and validated within a human-robot interaction application: gaze tracing for artificial attention systems.
... The robot, binding visual (saliency map with motion) and proprioceptive (accelerometers) sensory contingencies, detects whether a pixel belongs to itself or not. This layer combines probabilistic inference grids with attentional maps [40]. ...
... On one hand, the image stream provided by the camera is processed by a visual attention system [21], that contributes with two main outputs: the saliency map with the protoobject relevance encoded in a 2D image and a list of attended protoobjects in the working memory. The spatial saliency is computed using several conspicuity maps that represent different features of the protoobjects [40]: color and intensity contrast, optical flow and color bias. These feature maps (2D images) are combined by weighted average using a fixed attentional set (weights), which in the case of having contextual information is used for top-down modulation. ...
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We address self-perception in robots as the key for world understanding and causality interpretation. We present a self-perception mechanism that enables a humanoid robot to understand certain sensory changes caused by naive actions during interaction with objects. Visual, proprioceptive and tactile cues are combined via artificial attention and probabilistic reasoning to permit the robot to discern between inbody and outbody sources in the scene.With that support and exploiting inter-modal sensory contingencies, the robot can infer simple concepts such as discovering potential "usable" objects. Theoretically and through experimentation with a real humanoid robot, we show how self-perception is a backdrop ability for high order cognitive skills. Moreover, we present a novel model for self-detection, which does not need to track the body parts. Furthermore, results show that the proposed approach successfully discovers objects in the reaching space improving scene understanding by discriminating real objects from visual artefacts.
... For instance, computing where the human is looking at and where the robot should look at or which object should be grasped. Furthermore, multi-sensory and 3D saliency computation has also been investigated (Lanillos et al., 2015b). Finally, more complex attention behaviors, particularly designed for social robotics and based on human non-verbal communication, such as joint attention, have also been addressed. ...
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Computational models of visual attention in artificial intelligence and robotics have been inspired by the concept of a saliency map. These models account for the mutual information between the (current) visual information and its estimated causes. However, they fail to consider the circular causality between perception and action. In other words, they do not consider where to sample next, given current beliefs. Here, we reclaim salience as an active inference process that relies on two basic principles: uncertainty minimization and rhythmic scheduling. For this, we make a distinction between attention and salience. Briefly, we associate attention with precision control, i.e., the confidence with which beliefs can be updated given sampled sensory data, and salience with uncertainty minimization that underwrites the selection of future sensory data. Using this, we propose a new account of attention based on rhythmic precision-modulation and discuss its potential in robotics, providing numerical experiments that showcase its advantages for state and noise estimation, system identification and action selection for informative path planning.
... For instance, computing where the human is looking at and where the robot should look at or which object should be grasped. Furthermore, multi-sensory and 3D saliency computation has also been investigated [Lanillos et al., 2015b]. Finally, more complex attention behaviours, particularly designed for social robotics and based on human non-verbal communication, such as joint attention, have also been addressed. ...
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Full-text available
Computational models of visual attention in artificial intelligence and robotics have been inspired by the concept of a saliency map. These models account for the mutual information between the (current) visual information and its estimated causes. However, they fail to consider the circular causality between perception and action. In other words, they do not consider where to sample next, given current beliefs. Here, we reclaim salience as an active inference process that relies on two basic principles: uncertainty minimisation and rhythmic scheduling. For this, we make a distinction between attention and salience. Briefly, we associate attention with precision control, i.e., the confidence with which beliefs can be updated given sampled sensory data, and salience with uncertainty minimisation that underwrites the selection of future sensory data. Using this, we propose a new account of attention based on rhythmic precision-modulation and discuss its potential in robotics, providing numerical experiments that showcase advantages of precision-modulation for state and noise estimation, system identification and action selection for informative path planning.
... Results showed that the robot was able to discern between inbody and outbody sources without using markers or simplified segmentation. Figure 1 shows the proto-object saliency system [4] used as visual input and the computed probability of the image regions belonging to the robot arm. Body perception was formalized as an inference problem while the robot was interacting with the world. ...
Preprint
Artificial self-perception is the machine ability to perceive its own body, i.e., the mastery of modal and intermodal contingencies of performing an action with a specific sensors/actuators body configuration. In other words, the spatio-temporal patterns that relate its sensors (e.g. visual, proprioceptive, tactile, etc.), its actions and its body latent variables are responsible of the distinction between its own body and the rest of the world. This paper describes some of the latest approaches for modelling artificial body self-perception: from Bayesian estimation to deep learning. Results show the potential of these free-model unsupervised or semi-supervised crossmodal/intermodal learning approaches. However, there are still challenges that should be overcome before we achieve artificial multisensory body perception.
... 3D Attention. Many works have been done on 3D human attention[Sugano et al., 2014;Jeni and Cohn, 2016;FunesMora and Odobez, 2016;Mansouryar et al., 2016;Chen et al., 2008;Lanillos et al., 2015]. Funes-Mora and Odobez[Funes-Mora and Odobez, 2016]estimated gaze directions based on head poses and eye images. ...
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This paper addresses the problem of inferring 3D human attention in RGB-D videos at scene scale. 3D human attention describes where a human is looking in 3D scenes. We propose a probabilistic method to jointly model attention, intentions, and their interactions. Latent intentions guide human attention which conversely reveals the intention features. This mutual interaction makes attention inference a joint optimization with latent intentions. An EM-based approach is adopted to learn the latent intentions and model parameters. Given an RGB-D video with 3D human skeletons, a joint-state dynamic programming algorithm is utilized to jointly infer the latent intentions, the 3D attention directions, and the attention voxels in scene point clouds. Experiments on a new 3D human attention dataset prove the strength of our method.
... The robot, binding visual (saliency map with motion) and proprioceptive (accelerometers) cues, detects whether a pixel belongs to itself or not. This layer combines probabilistic inference grids with attentional maps [14]. To avoid the tracking of the robot parts 1st order dynamics (velocities) are learnt online. ...
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We address self-perception and object discovery by integrating multimodal tactile, proprioceptive and visual cues. Considering sensory signals as the only way to obtain relevant information about the environment, we enable a humanoid robot to infer potential usable objects relating visual self-detection with tactile cues. Hierarchical Bayesian models are combined with signal processing and protoobject artificial attention to tackle the problem. Results show that the robot is able to: (1) discern between inbody and outbody sources without using markers or simplified segmentation; (2) accurately discover objects in the reaching space; and (3) discriminate real objects from visual artefacts, aiding scene understanding. Furthermore, this approach reveals the need for several layers of abstraction for achieving agency and causality due to the inherent ambiguity of the sensory cues.
... Applications in education are envisioned when helping students in engineering to understand conventions in technical drawing. Other applications in computer vision would be interesting, for example when computing 3D attention saliency in proto-objects [42], a Q3D description could help to store a short memory narrative of the evolution of the proto-objects in these attention artificial systems. ...
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Given the amount and variety of saliency models, the knowledge of their pros and cons, the applications they are more suitable for, or which are the more challenging scenes for each of them, would be very useful for the progress in the field. This assessment can be done based on the link between algorithms and public datasets. In one hand, performance scores of algorithms can be used to cluster video samples according to the pattern of difficulties they pose to models. In the other hand, cluster labels can be combined with video annotations to select discriminant attributes for each cluster. In this work we seek this link and try to describe each cluster of videos in a few words.
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Although our sensory experience is mostly multisensory in nature, research on working memory representations has focused mainly on examining the senses in isolation. Results from the multisensory processing literature make it clear that the senses interact on a more intimate manner than previously assumed. These interactions raise questions regarding the manner in which multisensory information is maintained in working memory. We discuss the current status of research on multisensory processing and the implications of these findings on our theoretical understanding of working memory. To do so, we focus on reviewing working memory research conducted from a multisensory perspective, and discuss the relation between working memory, attention, and multisensory processing in the context of the predictive coding framework. We argue that a multisensory approach to the study of working memory is indispensable to achieve a realistic understanding of how working memory processes maintain and manipulate information.
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