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Alvaro Cepero, Albert Clapes, & Sergio Escalera. (2015). Automatic non-verbal communication skills analysis: a quantitative evaluation. AIC - AI Communications, 28(1), 87–101.
Abstract: The oral communication competence is defined on the top of the most relevant skills for one's professional and personal life. Because of the importance of communication in our activities of daily living, it is crucial to study methods to evaluate and provide the necessary feedback that can be used in order to improve these communication capabilities and, therefore, learn how to express ourselves better. In this work, we propose a system capable of evaluating quantitatively the quality of oral presentations in an automatic fashion. The system is based on a multi-modal RGB, depth, and audio data description and a fusion approach in order to recognize behavioral cues and train classifiers able to eventually predict communication quality levels. The performance of the proposed system is tested on a novel dataset containing Bachelor thesis' real defenses, presentations from an 8th semester Bachelor courses, and Master courses' presentations at Universitat de Barcelona. Using as groundtruth the marks assigned by actual instructors, our system achieves high performance categorizing and ranking presentations by their quality, and also making real-valued mark predictions.
Keywords: Social signal processing; human behavior analysis; multi-modal data description; multi-modal data fusion; non-verbal communication analysis; e-Learning
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Jianzhy Guo, Zhen Lei, Jun Wan, Egils Avots, Noushin Hajarolasvadi, Boris Knyazev, et al. (2018). Dominant and Complementary Emotion Recognition from Still Images of Faces. ACCESS - IEEE Access, 6, 26391–26403.
Abstract: Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition.
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Wenlong Deng, Yongli Mou, Takahiro Kashiwa, Sergio Escalera, Kohei Nagai, Kotaro Nakayama, et al. (2020). Vision based Pixel-level Bridge Structural Damage Detection Using a Link ASPP Network. AC - Automation in Construction, 110, 102973.
Abstract: Structural Health Monitoring (SHM) has greatly benefited from computer vision. Recently, deep learning approaches are widely used to accurately estimate the state of deterioration of infrastructure. In this work, we focus on the problem of bridge surface structural damage detection, such as delamination and rebar exposure. It is well known that the quality of a deep learning model is highly dependent on the quality of the training dataset. Bridge damage detection, our application domain, has the following main challenges: (i) labeling the damages requires knowledgeable civil engineering professionals, which makes it difficult to collect a large annotated dataset; (ii) the damage area could be very small, whereas the background area is large, which creates an unbalanced training environment; (iii) due to the difficulty to exactly determine the extension of the damage, there is often a variation among different labelers who perform pixel-wise labeling. In this paper, we propose a novel model for bridge structural damage detection to address the first two challenges. This paper follows the idea of an atrous spatial pyramid pooling (ASPP) module that is designed as a novel network for bridge damage detection. Further, we introduce the weight balanced Intersection over Union (IoU) loss function to achieve accurate segmentation on a highly unbalanced small dataset. The experimental results show that (i) the IoU loss function improves the overall performance of damage detection, as compared to cross entropy loss or focal loss, and (ii) the proposed model has a better ability to detect a minority class than other light segmentation networks.
Keywords: Semantic image segmentation; Deep learning
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Joakim Bruslund Haurum, Meysam Madadi, Sergio Escalera, & Thomas B. Moeslund. (2022). Multi-scale hybrid vision transformer and Sinkhorn tokenizer for sewer defect classification. AC - Automation in Construction, 144, 104614.
Abstract: A crucial part of image classification consists of capturing non-local spatial semantics of image content. This paper describes the multi-scale hybrid vision transformer (MSHViT), an extension of the classical convolutional neural network (CNN) backbone, for multi-label sewer defect classification. To better model spatial semantics in the images, features are aggregated at different scales non-locally through the use of a lightweight vision transformer, and a smaller set of tokens was produced through a novel Sinkhorn clustering-based tokenizer using distinct cluster centers. The proposed MSHViT and Sinkhorn tokenizer were evaluated on the Sewer-ML multi-label sewer defect classification dataset, showing consistent performance improvements of up to 2.53 percentage points.
Keywords: Sewer Defect Classification; Vision Transformers; Sinkhorn-Knopp; Convolutional Neural Networks; Closed-Circuit Television; Sewer Inspection
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Oriol Rodriguez-Leor, E. Fernandez-Nofrerias, J. Mauri, C. Garcia, R. Villuendas, V. Valle, et al. (2003). Intravascular ultrasound segmentation using local binary patterns. European Heart Journal (IF: 5.997), ESC Congress 2003.
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