1,720,967 research outputs found
Continual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentation
Full text linkIn the field of Earth Observation (EO), Continual Learning (CL) algorithms
have been proposed to deal with large datasets by decomposing them into several
subsets and processing them incrementally. The majority of these algorithms
assume that data is (a) coming from a single source, and (b) fully labeled.
Real-world EO datasets are instead characterized by a large heterogeneity
(e.g., coming from aerial, satellite, or drone scenarios), and for the most
part they are unlabeled, meaning they can be fully exploited only through the
emerging Self-Supervised Learning (SSL) paradigm. For these reasons, in this
paper we propose a new algorithm for merging SSL and CL for remote sensing
applications, that we call Continual Barlow Twins (CBT). It combines the
advantages of one of the simplest self-supervision techniques, i.e., Barlow
Twins, with the Elastic Weight Consolidation method to avoid catastrophic
forgetting. In addition, for the first time we evaluate SSL methods on a highly
heterogeneous EO dataset, showing the effectiveness of these strategies on a
novel combination of three almost non-overlapping domains datasets (airborne
Potsdam dataset, satellite US3D dataset, and drone UAVid dataset), on a crucial
downstream task in EO, i.e., semantic segmentation. Encouraging results show
the superiority of SSL in this setting, and the effectiveness of creating an
incremental effective pretrained feature extractor, based on ResNet50, without
the need of relying on the complete availability of all the data, with a
valuable saving of time and resources
Continual self-supervised learning in Earth observation with embedding regularization
No full textContinual Self-Supervised Learning (CSSL) is a promising approach for intelligent systems that address the challenge of learning in scenarios with limited data, mirroring real-world conditions. However, CSSL remains relatively unexplored, especially in the context of Earth Observation (EO). In this paper, we investigate the problem of CSSL in remote sensing (RS), focusing on leveraging satellite and aerial imagery to develop systems that can continuously adapt and learn with minimal human intervention in data preparation. Specifically, we tackle the task of semantic segmentation, which has diverse applications in RS. Building upon existing work in the domain, we propose a novel algorithm called Continual Barlow Twins with Embedding Regularizer (CBT-ER). To evaluate the effectiveness of our approach, we conduct experiments on three heterogeneous datasets (i.e. Potsdam, DFC2022, SEN12MS). To ensure robust experimentation, we vary the availability of data labels (10%, 100%) and compare our approach against different baselines, showing encouraging performance
GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates
No full textLand cover maps are a pivotal element in a wide range of Earth Observation (EO) applications. However, anno- tating large datasets to develop supervised systems for re- mote sensing (RS) semantic segmentation is costly and time- consuming. Unsupervised Domain Adaption (UDA) could tackle these issues by adapting a model trained on a source domain, where labels are available, to a target domain, without annotations. UDA, while gaining importance in computer vision, is still under-investigated in RS. Thus, we propose a new lightweight model, GeoMultiTaskNet, based on two contributions: a GeoMultiTask module (GeoMT), which utilizes geographical coordinates to align the source and target domains, and a Dynamic Class Sampling (DCS) strategy, to adapt the semantic segmentation loss to the fre- quency of classes. This approach is the first to use geo- graphical metadata for UDA in semantic segmentation. It reaches state-of-the-art performances (47,22% mIoU), re- ducing at the same time the number of parameters (33M), on a subset of the FLAIR dataset, a recently proposed dataset properly shaped for RS UDA, used for the first time ever for research scopes here
Board diversity and firm performance: an empirical analysis of Italian small-medium enterprise
Full text linkThis paper aims to empirically verify if the board of directors’ (BoD) diversity (i.e., gender, age, and nationality) affects firm performance, which we calculate referring to ROE, ROA, and EBITDA margin. So far, scholars do not converge on a single answer about the effects of observable diversity in the boardrooms on corporate performance. Therefore, this study — referring to a significantly bigger sample — applies machine learning models following a data-driven approach based on a three-year (2017–2019) dataset composed of 59,229 Italian small-medium enterprises (SMEs). The analysis conducted shows that board
diversity does not impact firm results, either positively or negatively. The lack of a correlation suggests that there is no reason to not appoint females, young people, and foreigners as directors. The nvolvement of these ―minorities‖, which, as shown, does not negatively impact economic-financial results, could on the opposite improve firm reputation as well as enhance the intellectual capital, solving in the meantime a social matter
Inferring 3D change detection from bitemporal optical images
No full textIn recent years, change detection (CD) using deep learning (DL) algorithms has been a very active research topic in the field of remote sensing (RS). Nevertheless, the CD algorithms developed so far are mainly focused on generating two-dimensional (2D) change maps where the planimetric extent of the areas affected by changes is identified without providing any information on the corresponding elevation variations. The aim of this work is, hence, to establish the basis for the development of DL algorithms able to automatically generate an elevation (3D) CD map along with a standard 2D CD map, using only bitemporal optical images as input, and thus without the need to rely directly on elevation data during the inference phase. Specifically, our work proposes a novel network, capable of solving the 2D and 3D CD tasks simultaneously, and a modified version of the 3DCD dataset, a freely available dataset designed precisely for this twofold task. The proposed architecture consists of a Transformer network based on a semantic tokenizer: the MultiTask Bitemporal Images Transformer (MTBIT). Encouraging results, obtained on the modified version of the 3DCD dataset by comparing the proposed architecture with other networks specifically designed to solve the 2D CD task, are shown. In particular, MTBIT achieves a metric accuracy (represented by the changed root mean squared error) equal to 6.46 m – the best performance among the compared architectures – with a limited number of parameters (13,1 M). The code and the 3DCD dataset are available at https://sites.google.com/uniroma1.it/3dchangedetection/home-page
Conditional computation in neural networks: Principles and research trends
Full text linkThis article summarizes principles and ideas from the emerging area of applying conditional computation methods to the design of neural networks. In particular, we focus on neural networks that can dynamically activate or de-activate parts of their computational graph conditionally on their input. Examples include the dynamic selection of, e.g., input tokens, layers (or sets of layers), and sub-modules inside each layer (e.g., channels in a convolutional filter). We first provide a general formalism to describe these techniques in an uniform way. Then, we introduce three notable implementations of these principles: mixture-of-experts (MoEs) networks, token selection mechanisms, and early-exit neural networks. The paper aims to provide a tutorial-like introduction to this growing field. To this end, we analyze the benefits of these modular designs in terms of efficiency, explainability, and transfer learning, with a focus on emerging applicative areas ranging from automated scientific discovery to semantic communication
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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