Toward Foundation Models for Earth Monitoring: Generalizable Deep Learning Models for Natural Hazard Segmentation

Climate change results in an increased probability of extreme weather events that put societies and businesses at risk on a global scale. Therefore, near real-time mapping of natural hazards is an emerging priority for the support of natural disaster relief, risk management, and informing governmental policy decisions. Recent methods to achieve near real-time mapping increasingly leverage deep learning (DL). However, DL-based approaches are designed for one specific task in a single geographic region based on specific frequency bands of satellite data. Therefore, DL models used to map specific natural hazards struggle with their generalization to other types of natural hazards in unseen regions. In this work, we propose a methodology to significantly improve the generalizability of DL natural hazards mappers based on pre-training on a suitable pre-task. Without access to any data from the target domain, we demonstrate this improved generalizability across four U-Net architectures for the segmentation of unseen natural hazards. Importantly, our method is invariant to geographic differences and differences in the type of frequency bands of satellite data. By leveraging characteristics of unlabeled images from the target domain that are publicly available, our approach is able to further improve the generalization behavior without fine-tuning. Thereby, our approach supports the development of foundation models for earth monitoring with the objective of directly segmenting unseen natural hazards across novel geographic regions given different sources of satellite imagery.Comment: Accepted at IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2023

Multimodal Affect and Aesthetic Experience

The term “aesthetic experience” corresponds to the inner state of a person exposed to form and content of artistic objects. Exploring certain aesthetic values of artistic objects, as well as interpreting the aesthetic experience of people when exposed to art can contribute towards understanding (a) art and (b) people’s affective reactions to artwork. Focusing on different types of artistic content, such as movies, music, urban art and other artwork, the goal of this workshop is to enhance the interdisciplinary collaboration between affective computing and aesthetics researchers

Recognizing film aesthetics, spectators' affect and aesthetic emotions from multimodal signals

Even though aesthetic experiences area common in our lives, processes involved in aesthetic experience are not fully understood. Moreover, there is no comprehensive theory that explains and defines the concept of aesthetic experience in art. The challenge of studies on aesthetic experiences is to understand different stages of aesthetic information processing, such as perceptual analysis, cognitive processes, and evaluation resulting in aesthetic judgments and emotions. The main goal of this thesis is to analyze film aesthetic experience evoked in spectators. In particular, we aim to detect aesthetic highlights in movies, as well as recognize induced emotions and aesthetic emotions elicited in spectators. The outcomes of the research on induced emotions, aesthetic emotions, and aesthetic highlights allow researchers to better understand processes involved in film aesthetic experience and can be used for emotional and aesthetic scene detection, emotional and aesthetic scene design, video summarization, and prediction of affective and aesthetic content

A reconfigurable integrated electronic tongue and its use in accelerated analysis of juices and wines

Potentiometric electronic tongues (ETs) leveraging trends in miniaturization and internet of things (IoT) bear promise for facile mobile chemical analysis of complex multicomponent liquids, such as beverages. In this work, hand-crafted feature extraction from the transient potentiometric response of an array of low-selective miniaturized polymeric sensors is combined with a data pipeline for deployment of trained machine learning models on a cloud back-end or edge device. The sensor array demonstrated sensitivity to different organic acids and exhibited interesting performance for the fingerprinting of fruit juices and wines, including differentiation of samples through supervised learning based on sensory descriptors and prediction of consumer acceptability of aged juice samples. Product authentication, quality control and support of sensory evaluation are some of the applications that are expected to benefit from integrated electronic tongues that facilitate the characterization of complex properties of multi-component liquids

Synchronization among Groups of Spectators for Highlight Detection in Movies

Detection of emotional and aesthetic highlights is a challenge for the affective understanding of movies. Our assumption is that synchronized spectators' physiological and behavioral reactions occur during these highlights. We propose to employ the periodicity score to capture synchronization among groups of spectators' signals. To uncover the periodicity score's capabilities, we compare it with baseline synchronization measures, such as the nonlinear interdependence and the windowed mutual information. The results show that the periodicity score and the pairwise synchronization measures are able to capture different properties of spectators' synchronization, and they indicate the presence of some types of emotional and aesthetic highlights in a movie based on spectators' electro-dermal and acceleration signals

Dynamic Time Warping of Multimodal Signals for Detecting Highlights in Movies

Affective computing has strong ties with literature and film studies, e.g. text sentiment analysis, affective tagging of movies. In this work we report on recent findings towards identifying highlights in movies on the basis of the synchronization of physiological and behavioral signals of people. The proposed architecture is utilizing dynamic time warping for measuring the distance among the multimodal signals of pairs of spectators. The reported results suggest that this distance can be indicative for the dynamics and existence of aesthetic moments in movies

Identifying aesthetic highlights in movies from clustering of physiological and behavioral signals

Affective computing is an important research area of computer science, with strong ties with humanities in particular. In this work we detail recent research activities towards determining moments of aesthetic importance in movies, on the basis of the reactions of multiple spectators. These reactions correspond to the multimodal reaction profile of a group of people and are computed from their physiological and behavioral signals. The highlight identification system using the reaction profile is evaluated on the basis of annotated aesthetic moments. The proposed architecture shows significant ability to determine moments of aesthetic importance, despite the challenges resulting from its operation in ecological situation, i.e. real-life recordings of the reactions of spectators watching a film in the movie theate

Spectators' Synchronization Detection based on Manifold Representation of Physiological Signals: Application to Movie Highlights Detection

Detection of highlights in movies is a challenge for the affective understanding and implicit tagging of films. Under the hypothesis that synchronization of the reaction of spectators indicates such highlights, we define a synchronization measure between spectators that is capable of extracting movie highlights. The intuitive idea of our approach is to define (a) a parameterization of one spectator's physiological data on a manifold; (b) the synchronization measure between spectators as the Kolmogorov-Smirnov distance between local shape distributions of the underlying manifolds. We evaluate our approach using data collected in an experiment where the electro-dermal activity of spectators was recorded during the entire projection of a movie in a cinema. We compare our methodology with baseline synchronization measures, such as correlation, Spearman's rank correlation, mutual information, Kolmogorov-Smirnov distance. Results indicate that the proposed approach allows to accurately distinguish highlight from non-highlight scenes