Miguel Colom | Searcharxiv

arXiv2026

Combi-CAM: A Novel Multi-Layer Approach for Explainable Image Geolocalization

Planet-scale photo geolocalization involves the intricate task of estimating the geographic location depicted in an image purely based on its visual features. While deep learning models, particularly convolutional neural networks (CNNs), have significantly advanced this field, understanding the reasoning behind their predictions remains challenging. In this paper, we present Combi-CAM, a novel method that enhances the explainability of CNN-based geolocalization models by combining gradient-weighted class activation maps obtained from several layers of the network architecture, rather than using only information from the deepest layer as is typically done. This approach provides a more detailed understanding of how different image features contribute to the model's decisions, offering deeper insights than the traditional approaches.

cs.CV↗

arXiv2023

Repeatability, Reproducibility, Replicability, Reusability (4R) in Journals' Policies and Software/Data Management in Scientific Publications: A Survey, Discussion, and Perspectives

With the recognized crisis of credibility in scientific research, there is a growth of reproducibility studies in computer science, and although existing surveys have reviewed reproducibility from various perspectives, especially very specific technological issues, they do not address the author-publisher relationship in the publication of reproducible computational scientific articles. This aspect requires significant attention because it is the basis for reliable research. We have found a large gap between the reproducibility-oriented practices, journal policies, recommendations, publisher artifact Description/Evaluation guidelines, submission guides, technological reproducibility evolution, and its effective adoption to contribute to tackling the crisis. We conducted a narrative survey, a comprehensive overview and discussion identifying the mutual efforts required from Authors, Journals, and Technological actors to achieve reproducibility research. The relationship between authors and scientific journals in their mutual efforts to jointly improve the reproducibility of scientific results is analyzed. Eventually, we propose recommendations for the journal policies, as well as a unified and standardized Reproducibility Guide for the submission of scientific articles for authors. The main objective of this work is to analyze the implementation and experiences of reproducibility policies, techniques and technologies, standards, methodologies, software, and data management tools required for scientific reproducible publications. Also, the benefits and drawbacks of such an adoption, as well as open challenges and promising trends, to propose possible strategies and efforts to mitigate the identified gaps. To this purpose, we analyzed 200 scientific articles, surveyed 16 Computer Science journals, and systematically classified them according to reproducibility strategies, technologies, policies, code citation, and editorial business. We conclude there is still a reproducibility gap in scientific publications, although at the same time also the opportunity to reduce this gap with the joint effort of authors, publishers, and technological providers.

cs.SE↗

arXiv2021

Non-Semantic Evaluation of Image Forensics Tools: Methodology and Database

With the aim of evaluating image forensics tools, we propose a methodology to create forgeries traces, leaving intact the semantics of the image. Thus, the only forgery cues left are the specific alterations of one or several aspects of the image formation pipeline. This methodology creates automatically forged images that are challenging to detect for forensic tools and overcomes the problem of creating convincing semantic forgeries. Based on this methodology, we create the Trace database and conduct an evaluation of the main state-of-the-art image forensics tools.

eess.IV↗