Deep Affordance-grounded Sensorimotor Object Recognition

It is well-established by cognitive neuroscience that human perception of objects constitutes a complex process, where object appearance information is combined with evidence about the so-called object "affordances", namely the types of actions that humans typically perform when interacting with them. This fact has recently motivated the "sensorimotor" approach to the challenging task of automatic object recognition, where both information sources are fused to improve robustness. In this work, the aforementioned paradigm is adopted, surpassing current limitations of sensorimotor object recognition research. Specifically, the deep learning paradigm is introduced to the problem for the first time, developing a number of novel neuro-biologically and neuro-physiologically inspired architectures that utilize state-of-the-art neural networks for fusing the available information sources in multiple ways. The proposed methods are evaluated using a large RGB-D corpus, which is specifically collected for the task of sensorimotor object recognition and is made publicly available. Experimental results demonstrate the utility of affordance information to object recognition, achieving an up to 29% relative error reduction by its inclusion.Comment: 9 pages, 7 figures, dataset link included, accepted to CVPR 201

Hierarchical Detection of Sound Events and their Localization Using Convolutional Neural Networks with Adaptive Thresholds

This paper details our approach to Task 3 of the DCASE’19 Challenge, namely sound event localization and detection (SELD). Our system is based on multi-channel convolutional neural networks (CNNs), combined with data augmentation and ensembling. Specifically, it follows a hierarchical approach that first determines adaptive thresholds for the multi-label sound event detection (SED) problem, based on a CNN operating on spectrograms over long duration windows. It then exploits the derived thresholds in an ensemble of CNNs operating on raw waveforms over shorter-duration sliding windows to provide event segmentation and labeling. Finally, it employs event localization CNNs to yield direction-of-arrival (DOA) source estimates of the detected sound events. The system is developed and evaluated on the microphone-array set of Task 3. Compared to the baseline of the Challenge organizers, on the development set it achieves relative improvements of 12% in SED error, 2% in F-score, 36% in DOA error, and 3% in the combined SELD metric, but trails significantly in frame-recall, whereas on the evaluation set it achieves relative improvements of 3% in SED, 51% in DOA, and 4% in SELD errors. Overall though, the system lags significantly behind the best Task 3 submission, achieving a combined SELD error of 0.2033 against 0.044 of the latter505

A Unified Approach to Multi-Pose Audio-Visual ASR

The vast majority of studies in the field of audio-visual automatic speech recognition (AVASR) assumes frontal images of a speaker's face, but this cannot always be guaranteed in practice. Hence our recent research efforts have concentrated on extracting visual speech information from non-frontal faces, in particular the profile view. The introduction of additional views to an AVASR system increases the complexity of the system, as it has to deal with the different visual features associated with the various views. In this paper, we propose the use of linear regression to find a transformation matrix based on synchronous frontal and profile visual speech data, which is used to normalize the visual speech in each viewpoint into a single uniform view. In our experiments for the task of multi-speaker lipreading, we show that this "pose-invariant" technique reduces train/test mismatch between visual speech features of different views, and is of particular benefit when there is more training data for one viewpoint over another (e.g. frontal over profile)

A Deep Learning Approach to Object Affordance Segmentation

Learning to understand and infer object functionalities is an important step towards robust visual intelligence. Significant research efforts have recently focused on segmenting the object parts that enable specific types of human-object interaction, the so-called "object affordances". However, most works treat it as a static semantic segmentation problem, focusing solely on object appearance and relying on strong supervision and object detection. In this paper, we propose a novel approach that exploits the spatio-temporal nature of human-object interaction for affordance segmentation. In particular, we design an autoencoder that is trained using ground-truth labels of only the last frame of the sequence, and is able to infer pixel-wise affordance labels in both videos and static images. Our model surpasses the need for object labels and bounding boxes by using a soft-attention mechanism that enables the implicit localization of the interaction hotspot. For evaluation purposes, we introduce the SOR3D-AFF corpus, which consists of human-object interaction sequences and supports 9 types of affordances in terms of pixel-wise annotation, covering typical manipulations of tool-like objects. We show that our model achieves competitive results compared to strongly supervised methods on SOR3D-AFF, while being able to predict affordances for similar unseen objects in two affordance image-only datasets.Comment: 5 pages, 4 figures, ICASSP 202

Audio-Visual ASR from Multiple Views Inside Smart Rooms

Visual information from a speaker's mouth region is known to improve automatic speech recognition robustness. However, the vast majority of audio-visual automatic speech recognition (AVASR) studies assume frontal images of the speaker's face, which is not always the case in realistic human-computer interaction (HCI) scenarios. One such case of interest is HCI inside smart rooms, equipped with pan-tilt-zoom (PTZ) cameras that closely track the subject's head. Since however these cameras are fixed in space, they cannot necessarily obtain frontal views of the speaker. Clearly, AVASR from non-frontal views is required, as well as fusion of multiple camera views, if available. In this paper, we report our very preliminary work on this subject. In particular, we concentrate on two topics: first, the design of an AVASR system that operates on profile face views and its comparison with a traditional frontal-view AVASR system, and second, the fusion of the two systems into a multi-view frontal/profile system. We in particular describe our visual front end approach for the profile view system, and report experiments on a multi-subject, small-vocabulary, bimodal, multi-sensory database that contains synchronously captured audio with frontal and profile face video, recorded inside the IBM smart room as part of the CHIL project. Our experiments demonstrate that AVASR is possible from profile views, however the visual modality benefit is decreased compared to frontal video data

Patch-based analysis of visual speech from multiple views

Obtaining a robust feature representation of visual speech is of crucial importance in the design of audio-visual automatic speech recognition systems. In the literature, when visual appearance based features are employed for this purpose, they are typically extracted using a "holistic" approach. Namely, a transformation of the pixel values of the entire region-of-interest (ROI) is obtained, with the ROI covering the speaker's mouth and often surrounding facial area. In this paper, we instead consider a "patch" based visual feature extraction approach, within the appearance based framework. In particular, we conduct a novel analysis to determine which areas (patches) of the mouth ROI are the most informative for visual speech. Furthermore, we extend this analysis beyond the traditional frontal views, by investigating profile views as well. Not surprisingly, and for both frontal and profile views, we conclude that the central mouth patches are the most informative, but less so than the holistic features of the entire ROI. Nevertheless, fusion of holistic and the best patch based features further improves visual speech recognition performance, compared to either feature set alone. Finally, we discuss scenarios where the patch based approach may be preferable to holistic features

Lip-reading Using Profile Versus Frontal Views

Visual information from a speaker's mouth region is known to improve automatic speech recognition robustness. However, the vast majority of audio-visual automatic speech recognition (AVASR) studies assume frontal images of the speaker's face. In contrast, this paper investigates extracting visual speech information from the speaker's profile view, and, to our knowledge, constitutes the first real attempt to attack this problem. As with any AVASR system, the overall recognition performance depends heavily on the visual front end. This is especially the case with profile-view data, as the facial features are heavily compacted compared to the frontal scenario. In this paper, we particularly describe our visual front end approach, and report experiments on a multi-subject, small-vocabulary, bimodal, multisensory database that contains synchronously captured audio with frontal and profile face video. Our experiments show that AVASR is possible from profile views with moderate performance degradation compared to frontal video data