Hugo Jair Escalante, Sergio Escalera, Isabelle Guyon, Xavier Baro, Yagmur Gucluturk, Umut Guçlu, et al. (2018). Explainable and Interpretable Models in Computer Vision and Machine Learning.
Abstract: This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.
Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision.
This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following:
·Evaluation and Generalization in Interpretable Machine Learning
·Explanation Methods in Deep Learning
·Learning Functional Causal Models with Generative Neural Networks
·Learning Interpreatable Rules for Multi-Label Classification
·Structuring Neural Networks for More Explainable Predictions
·Generating Post Hoc Rationales of Deep Visual Classification Decisions
·Ensembling Visual Explanations
·Explainable Deep Driving by Visualizing Causal Attention
·Interdisciplinary Perspective on Algorithmic Job Candidate Search
·Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions
·Inherent Explainability Pattern Theory-based Video Event Interpretations
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G.D. Evangelidis, Ferran Diego, Joan Serrat, & Antonio Lopez. (2011). Slice Matching for Accurate Spatio-Temporal Alignment. In In ICCV Workshop on Visual Surveillance.
Abstract: Video synchronization and alignment is a rather recent topic in computer vision. It usually deals with the problem of aligning sequences recorded simultaneously by static, jointly- or independently-moving cameras. In this paper, we investigate the more difficult problem of matching videos captured at different times from independently-moving cameras, whose trajectories are approximately coincident or parallel. To this end, we propose a novel method that pixel-wise aligns videos and allows thus to automatically highlight their differences. This primarily aims at visual surveillance but the method can be adopted as is by other related video applications, like object transfer (augmented reality) or high dynamic range video. We build upon a slice matching scheme to first synchronize the sequences, while we develop a spatio-temporal alignment scheme to spatially register corresponding frames and refine the temporal mapping. We investigate the performance of the proposed method on videos recorded from vehicles driven along different types of roads and compare with related previous works.
Keywords: video alignment
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Maedeh Aghaei, Mariella Dimiccoli, & Petia Radeva. (2017). All the people around me: face clustering in egocentric photo streams. In 24th International Conference on Image Processing.
Abstract: arxiv1703.01790
Given an unconstrained stream of images captured by a wearable photo-camera (2fpm), we propose an unsupervised bottom-up approach for automatic clustering appearing faces into the individual identities present in these data. The problem is challenging since images are acquired under real world conditions; hence the visible appearance of the people in the images undergoes intensive variations. Our proposed pipeline consists of first arranging the photo-stream into events, later, localizing the appearance of multiple people in them, and
finally, grouping various appearances of the same person across different events. Experimental results performed on a dataset acquired by wearing a photo-camera during one month, demonstrate the effectiveness of the proposed approach for the considered purpose.
Keywords: face discovery; face clustering; deepmatching; bag-of-tracklets; egocentric photo-streams
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J. Elder, Fadi Dornaika, Y. Hou, & R. Goldstein. (2005). Attentive wide-field sensing for visual telepresence and surveillance. In L. Itti, G. Rees and J. Tsotsos (editors), Neurobiology of Attention, Academic Press / Elsevier.
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Anna Esposito, Italia Cirillo, Antonietta Esposito, Leopoldina Fortunati, Gian Luca Foresti, Sergio Escalera, et al. (2020). Impairments in decoding facial and vocal emotional expressions in high functioning autistic adults and adolescents. In Faces and Gestures in E-health and welfare workshop (pp. 667–674).
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Sergio Escalera, Xavier Baro, Jordi Vitria, Petia Radeva, & Bogdan Raducanu. (2012). Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction. SENS - Sensors, 12(2), 1702–1719.
Abstract: IF=1.77 (2010)
Social interactions are a very important component in peopleís lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Timesí Blogging Heads opinion blog.
The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The linksí weights are a measure of the ìinfluenceî a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network.
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Sergio Escalera, Xavier Baro, Oriol Pujol, Jordi Vitria, & Petia Radeva. (2011). Traffic-Sign Recognition Systems. Springer London.
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Sergio Escalera, Xavier Baro, Jordi Gonzalez, Miguel Angel Bautista, Meysam Madadi, Miguel Reyes, et al. (2014). ChaLearn Looking at People Challenge 2014: Dataset and Results. In ECCV Workshop on ChaLearn Looking at People (Vol. 8925, pp. 459–473). LNCS.
Abstract: This paper summarizes the ChaLearn Looking at People 2014 challenge data and the results obtained by the participants. The competition was split into three independent tracks: human pose recovery from RGB data, action and interaction recognition from RGB data sequences, and multi-modal gesture recognition from RGB-Depth sequences. For all the tracks, the goal was to perform user-independent recognition in sequences of continuous images using the overlapping Jaccard index as the evaluation measure. In this edition of the ChaLearn challenge, two large novel data sets were made publicly available and the Microsoft Codalab platform were used to manage the competition. Outstanding results were achieved in the three challenge tracks, with accuracy results of 0.20, 0.50, and 0.85 for pose recovery, action/interaction recognition, and multi-modal gesture recognition, respectively.
Keywords: Human Pose Recovery; Behavior Analysis; Action and in- teractions; Multi-modal gestures; recognition
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Sergio Escalera, Xavier Baro, Hugo Jair Escalante, & Isabelle Guyon. (2017). ChaLearn Looking at People: A Review of Events and Resources. In 30th International Joint Conference on Neural Networks.
Abstract: This paper reviews the historic of ChaLearn Looking at People (LAP) events. We started in 2011 (with the release of the first Kinect device) to run challenges related to human action/activity and gesture recognition. Since then we have regularly organized events in a series of competitions covering all aspects of visual analysis of humans. So far we have organized more than 10 international challenges and events in this field. This paper reviews associated events, and introduces the ChaLearn LAP platform where public resources (including code, data and preprints of papers) related to the organized events are available. We also provide a discussion on perspectives of ChaLearn LAP activities.
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Sergio Escalera, Stephane Ayache, Jun Wan, Meysam Madadi, Umut Guçlu, & Xavier Baro. (2019). Inpainting and Denoising Challenges.
Abstract: The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/video processing, such as denoising, restoration, super-resolution, or inpainting.
Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration.
This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapters present results from the Challenge, including three competition tasks at WCCI and ECML 2018. The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting.
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Anna Esposito, Terry Amorese, Nelson Maldonato, Alessandro Vinciarelli, Maria Ines Torres, Sergio Escalera, et al. (2020). Seniors’ ability to decode differently aged facial emotional expressions. In Faces and Gestures in E-health and welfare workshop (pp. 716–722).
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Sergio Escalera, Vassilis Athitsos, & Isabelle Guyon. (2017). Challenges in Multi-modal Gesture Recognition. (pp. 1–60).
Abstract: This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011–2015. We began right at the start of the Kinect TMTM revolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.
Keywords: Gesture recognition; Time series analysis; Multimodal data analysis; Computer vision; Pattern recognition; Wearable sensors; Infrared cameras; Kinect TMTM
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Sergio Escalera, Vassilis Athitsos, & Isabelle Guyon. (2016). Challenges in multimodal gesture recognition. JMLR - Journal of Machine Learning Research, 17, 1–54.
Abstract: This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. We began right at the start of the KinectTMrevolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands
of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.
Keywords: Gesture Recognition; Time Series Analysis; Multimodal Data Analysis; Computer Vision; Pattern Recognition; Wearable sensors; Infrared Cameras; KinectTM
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Rada Deeb, Joost Van de Weijer, Damien Muselet, Mathieu Hebert, & Alain Tremeau. (2019). Deep spectral reflectance and illuminant estimation from self-interreflections. JOSA A - Journal of the Optical Society of America A, 31(1), 105–114.
Abstract: In this work, we propose a convolutional neural network based approach to estimate the spectral reflectance of a surface and spectral power distribution of light from a single RGB image of a V-shaped surface. Interreflections happening in a concave surface lead to gradients of RGB values over its area. These gradients carry a lot of information concerning the physical properties of the surface and the illuminant. Our network is trained with only simulated data constructed using a physics-based interreflection model. Coupling interreflection effects with deep learning helps to retrieve the spectral reflectance under an unknown light and to estimate spectral power distribution of this light as well. In addition, it is more robust to the presence of image noise than classical approaches. Our results show that the proposed approach outperforms state-of-the-art learning-based approaches on simulated data. In addition, it gives better results on real data compared to other interreflection-based approaches.
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Michal Drozdzal, Jordi Vitria, Santiago Segui, Carolina Malagelada, Fernando Azpiroz, & Petia Radeva. (2014). Intestinal event segmentation for endoluminal video analysis. In 21st IEEE International Conference on Image Processing (pp. 3592–3596).
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