<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Kostas Georgiou</title><description>ML engineer with a PhD. Research, side projects, and open source on self-supervised learning, LLMs, and computer vision. (Feed contents: blog, changelog, publications, projects.)</description><link>https://gkos.dev/</link><item><title>[Paper] ExPLoRe: Exploration-driven Pre-training for Long-range Remote Sensing</title><link>https://gkos.dev/publications/#explore-2026/</link><guid isPermaLink="true">https://gkos.dev/publications/#explore-2026/</guid><description>ExPLoRe presents an exploration-driven pre-training strategy designed to capture long-range spatial dependencies in remote sensing imagery. The approach leverages structured exploration of multi-scale spatial contexts during self-supervised pre-training, improving performance on downstream segmentation and detection tasks.</description><pubDate>Thu, 31 Dec 2026 23:59:59 GMT</pubDate><category>ECCV 2026 (Under Review)</category></item><item><title>[Paper] MEDiC: Multi-objective Exploration of Distillation from CLIP</title><link>https://arxiv.org/abs/2603.29009</link><guid isPermaLink="true">https://arxiv.org/abs/2603.29009</guid><description>MEDiC introduces a multi-objective framework for distilling knowledge from CLIP into smaller, task-specific models for remote sensing applications. By jointly optimizing multiple objectives during distillation, the method achieves strong performance on downstream tasks while significantly reducing computational requirements.</description><pubDate>Thu, 31 Dec 2026 23:59:59 GMT</pubDate><category>arXiv preprint</category></item><item><title>[Changelog] Smaller, lighter site</title><link>https://gkos.dev/changelog/#2026-06-04-smaller-lighter-site/</link><guid isPermaLink="true">https://gkos.dev/changelog/#2026-06-04-smaller-lighter-site/</guid><description>Cleaned up a few half-built things: removed the experimental Hero Lab and the internal widgets showcase, renamed the level editor to /explore-editor, and tightened a couple of stale routes.</description><pubDate>Thu, 04 Jun 2026 11:00:00 GMT</pubDate></item><item><title>[Changelog] Stats page, properly wired</title><link>https://gkos.dev/changelog/#2026-06-03-stats-properly-wired/</link><guid isPermaLink="true">https://gkos.dev/changelog/#2026-06-03-stats-properly-wired/</guid><description>Every card on the stats page now pulls real data: site views from Cloudflare, top pages from Umami, reactions from the new backend, contributions from GitHub, downloads from PyPI and HuggingFace. No more placeholders.</description><pubDate>Wed, 03 Jun 2026 11:00:00 GMT</pubDate></item><item><title>[Changelog] React to blog posts</title><link>https://gkos.dev/changelog/#2026-06-02-blog-reactions/</link><guid isPermaLink="true">https://gkos.dev/changelog/#2026-06-02-blog-reactions/</guid><description>Each blog post now has an emoji picker at the end. 👍 ❤️ 🎉 💡 — your reactions accumulate and show up on the stats page.</description><pubDate>Tue, 02 Jun 2026 11:00:00 GMT</pubDate></item><item><title>[Changelog] Home page, less duplication</title><link>https://gkos.dev/changelog/#2026-05-22-home-less-duplication/</link><guid isPermaLink="true">https://gkos.dev/changelog/#2026-05-22-home-less-duplication/</guid><description>Resolved the two &quot;Workbench&quot; widgets on the home page. Now there&apos;s one Workbench card (the tools I use) and one Projects card (the work I&apos;ve built). Each links to its own page.</description><pubDate>Fri, 22 May 2026 11:00:00 GMT</pubDate></item><item><title>[Changelog] Projects, tightened</title><link>https://gkos.dev/changelog/#2026-05-15-projects-tightened/</link><guid isPermaLink="true">https://gkos.dev/changelog/#2026-05-15-projects-tightened/</guid><description>The projects page leads with the strongest work now. Reordered featured, renamed &quot;ML Research&quot; to &quot;Machine Learning,&quot; and moved older educational repos (the Numpy CNN, the p5 shooter) to the bottom.</description><pubDate>Fri, 15 May 2026 11:00:00 GMT</pubDate></item><item><title>[Changelog] Inspirations, properly curated</title><link>https://gkos.dev/changelog/#2026-05-03-inspirations-curated/</link><guid isPermaLink="true">https://gkos.dev/changelog/#2026-05-03-inspirations-curated/</guid><description>The inspirations page is now a real list of who and what I actually return to: mentors, researchers, books on the shelf, YouTube channels, podcasts, newsletters, and blogs. About 34 items, each with a link.</description><pubDate>Sun, 03 May 2026 11:00:00 GMT</pubDate></item><item><title>[Changelog] About page redesigned</title><link>https://gkos.dev/changelog/#2026-04-28-about-redesign/</link><guid isPermaLink="true">https://gkos.dev/changelog/#2026-04-28-about-redesign/</guid><description>The about page has a new shape: work history first with a rail timeline, then the story behind it in 8 chapters. A scroll-tracked curve weaves through every chapter.</description><pubDate>Tue, 28 Apr 2026 11:00:00 GMT</pubDate></item><item><title>[Changelog] Blog, Projects, and Publications redesigned</title><link>https://gkos.dev/changelog/#2026-04-26-blog-projects-publications/</link><guid isPermaLink="true">https://gkos.dev/changelog/#2026-04-26-blog-projects-publications/</guid><description>Redesigned the Blog, Projects, and Publications pages. Compact lists, click-to-expand details, and proper co-author and citation data pulled live.</description><pubDate>Sun, 26 Apr 2026 12:00:00 GMT</pubDate></item><item><title>[Changelog] Contact, Community Wall, and Changelog rebuilt</title><link>https://gkos.dev/changelog/#2026-04-25-contact-wall-changelog/</link><guid isPermaLink="true">https://gkos.dev/changelog/#2026-04-25-contact-wall-changelog/</guid><description>Rebuilt the Contact, Community Wall, and Changelog pages from the ground up. Cleaner layouts, better typography, and the new widgets in their natural habitat.</description><pubDate>Sat, 25 Apr 2026 11:00:00 GMT</pubDate></item><item><title>[Changelog] Widgets, redesigned</title><link>https://gkos.dev/changelog/#2026-04-24-widgets-redesign/</link><guid isPermaLink="true">https://gkos.dev/changelog/#2026-04-24-widgets-redesign/</guid><description>Reworked every widget on the site for a consistent look and feel: same chrome, same spacing, same hover states. A few new ones too.</description><pubDate>Fri, 24 Apr 2026 10:00:00 GMT</pubDate></item><item><title>[Changelog] Explore Mode: very soon™</title><link>https://gkos.dev/changelog/#2026-04-23-explore-soon/</link><guid isPermaLink="true">https://gkos.dev/changelog/#2026-04-23-explore-soon/</guid><description>The interactive version of the portfolio is nearly ready. Teaser hints live in the Hello World post.</description><pubDate>Thu, 23 Apr 2026 09:00:00 GMT</pubDate></item><item><title>[Changelog] KaTeX math in the blog</title><link>https://gkos.dev/changelog/#2026-04-22-math-in-blog/</link><guid isPermaLink="true">https://gkos.dev/changelog/#2026-04-22-math-in-blog/</guid><description>Blog posts can now render LaTeX equations through KaTeX. Inline and block math both work.</description><pubDate>Wed, 22 Apr 2026 12:00:00 GMT</pubDate></item><item><title>[Changelog] First two blog posts</title><link>https://gkos.dev/changelog/#2026-04-22-first-blog-posts/</link><guid isPermaLink="true">https://gkos.dev/changelog/#2026-04-22-first-blog-posts/</guid><description>Shipped Hello World and How I got here.</description><pubDate>Wed, 22 Apr 2026 11:00:00 GMT</pubDate></item><item><title>[Changelog] Site launch</title><link>https://gkos.dev/changelog/#2026-04-22-site-launch/</link><guid isPermaLink="true">https://gkos.dev/changelog/#2026-04-22-site-launch/</guid><description>The new portfolio is live. The first post, Hello World, walks through the history and what&apos;s new.</description><pubDate>Wed, 22 Apr 2026 10:00:00 GMT</pubDate></item><item><title>[Blog] How I got here</title><link>https://gkos.dev/blog/how-i-got-here/</link><guid isPermaLink="true">https://gkos.dev/blog/how-i-got-here/</guid><description>The long version of my background, from Greece to a PhD in Tennessee, with a few detours in between.</description><pubDate>Mon, 20 Apr 2026 04:00:00 GMT</pubDate><category>personal</category></item><item><title>[Blog] Hello world!</title><link>https://gkos.dev/blog/hello-world/</link><guid isPermaLink="true">https://gkos.dev/blog/hello-world/</guid><description>A fresh site, a fond farewell to the old one, and a small tease of what&apos;s coming next.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>personal</category><category>meta</category></item><item><title>[Project] MEDiC</title><link>https://huggingface.co/drkostas/MEDiC-ViT-Base</link><guid isPermaLink="true">https://huggingface.co/drkostas/MEDiC-ViT-Base</guid><description>Official PyTorch implementation of MEDiC: Multi-objective Exploration of Distillation from CLIP. Combines token distillation, CLS alignment, and pixel reconstruction with Evolved Part Masking. Achieves 85.07% finetuning and 73.92% k-NN on ImageNet-1K.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Machine Learning</category><category>PyTorch</category><category>Self-Supervised</category><category>CLIP</category><category>MIM</category><category>Computer-Vision</category><category>HuggingFace</category></item><item><title>[Project] Cross-scale MAE</title><link>https://github.com/aicip/Cross-Scale-MAE</link><guid isPermaLink="true">https://github.com/aicip/Cross-Scale-MAE</guid><description>Official code for the paper &apos;Cross-Scale MAE: A Tale of Multi-Scale Exploitation in Remote Sensing&apos;. Self-supervised learning for multi-scale geospatial imagery.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Machine Learning</category><category>PyTorch</category><category>MIM</category><category>Computer-Vision</category></item><item><title>[Project] MaskDistill-PyTorch</title><link>https://huggingface.co/drkostas/MaskDistill-ViT-Base</link><guid isPermaLink="true">https://huggingface.co/drkostas/MaskDistill-ViT-Base</guid><description>First open PyTorch reproduction of MaskDistill with pre-trained weights. Reproduces 84.8% finetuning accuracy (paper: 85.3%), with full evaluation suite: semantic segmentation, object detection, k-NN, and linear probe.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Machine Learning</category><category>PyTorch</category><category>Self-Supervised</category><category>CLIP</category><category>Computer-Vision</category><category>HuggingFace</category></item><item><title>[Project] Minecraft AI</title><link>https://github.com/drkostas/Minecraft-AI</link><guid isPermaLink="true">https://github.com/drkostas/Minecraft-AI</guid><description>A Reinforcement Learning agent that learns how to solve maze missions in Minecraft.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Machine Learning</category><category>PyTorch</category><category>Reinforcement-Learning</category><category>Minecraft</category></item><item><title>[Project] 3D Semantic Segmentation</title><link>https://github.com/drkostas/3D-Semantic-Segmentation</link><guid isPermaLink="true">https://github.com/drkostas/3D-Semantic-Segmentation</guid><description>Semantic Segmentation with Transformers on 3D Medical Images.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Machine Learning</category><category>PyTorch</category><category>OpenCV</category><category>SegFormer</category><category>Semantic-Segmentation</category><category>Medical-Imaging</category></item><item><title>[Project] BERT Question Answering</title><link>https://github.com/drkostas/Bert-Question-Answering</link><guid isPermaLink="true">https://github.com/drkostas/Bert-Question-Answering</guid><description>BERT-based question answering / reading comprehension methods on Rinehart Novels.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Machine Learning</category><category>PyTorch</category><category>SpaCy</category><category>HuggingFace</category><category>Transformers</category><category>BERT</category></item><item><title>[Project] Accident Severity Prediction</title><link>https://github.com/drkostas/accident-severity-prediction</link><guid isPermaLink="true">https://github.com/drkostas/accident-severity-prediction</guid><description>Predicting the severity of car accidents from various attributes.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Machine Learning</category><category>Pandas</category><category>Scipy</category><category>Bayesian-Optimization</category><category>XGBoost</category><category>Neural-Network</category></item><item><title>[Project] COVID-19 Vaccination Prediction</title><link>https://github.com/drkostas/covid19-vaccinations-predict</link><guid isPermaLink="true">https://github.com/drkostas/covid19-vaccinations-predict</guid><description>Simultaneous Time Series Forecasting on the global COVID-19 Daily Vaccinations.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Machine Learning</category><category>Tensorflow</category><category>LSTMs</category><category>Multivariate-Time-Series</category></item><item><title>[Project] Instagram Likes Prediction</title><link>https://github.com/drkostas/Insta-Likes-Predict</link><guid isPermaLink="true">https://github.com/drkostas/Insta-Likes-Predict</guid><description>First attempt on predicting the likes a photo will get on Instagram.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Machine Learning</category><category>Tensorflow</category><category>OpenCV</category><category>Instagram</category><category>Scraper</category><category>CNN</category></item><item><title>[Project] RL Value Iteration</title><link>https://github.com/drkostas/RL-Value-Iteration</link><guid isPermaLink="true">https://github.com/drkostas/RL-Value-Iteration</guid><description>Implementation of value iteration algorithm for calculating an optimal MDP policy.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Machine Learning</category><category>Markov-Decision-Process</category><category>Value-Iteration</category><category>RL</category></item><item><title>[Project] FleetSmart.ai</title><link>https://fleetsmart.ai</link><guid isPermaLink="true">https://fleetsmart.ai</guid><description>AI-powered fleet management platform for vessel tracking, compliance monitoring, and operational analytics.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Products</category><category>FastAPI</category><category>Next.js</category><category>GCP</category><category>LLM</category><category>PostgreSQL</category></item><item><title>[Project] ShiftMD</title><link>https://gkos.dev/projects/#shiftmd/</link><guid isPermaLink="true">https://gkos.dev/projects/#shiftmd/</guid><description>Intelligent shift scheduling system for medical departments using constraint programming optimization.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Products</category><category>Next.js</category><category>Python</category><category>OR-Tools</category><category>Supabase</category></item><item><title>[Project] XpensAI</title><link>https://xpensai.com</link><guid isPermaLink="true">https://xpensai.com</guid><description>AI-powered expense management platform with automated receipt scanning, OCR, and intelligent categorization.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Products</category><category>Python</category><category>AWS</category><category>Azure</category><category>GPT-4o</category><category>Serverless</category></item><item><title>[Project] Soma</title><link>https://soma-demo.gkos.dev</link><guid isPermaLink="true">https://soma-demo.gkos.dev</guid><description>Personal health and fitness dashboard aggregating data from Garmin, Strava, and Hevy into a unified analytics view.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Products</category><category>Python</category><category>Next.js</category><category>Garmin</category><category>Strava</category></item><item><title>[Project] Garmin Auth</title><link>https://github.com/drkostas/garmin-auth</link><guid isPermaLink="true">https://github.com/drkostas/garmin-auth</guid><description>Self-healing Garmin Connect OAuth authentication. Handles the complex SSO flow (OAuth1 to OAuth2), automatic token refresh, and rate limit recovery.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Tools</category><category>PyPi</category><category>Garmin</category><category>OAuth</category><category>Python</category><category>authentication</category></item><item><title>[Project] High SQL</title><link>https://github.com/drkostas/high-sql</link><guid isPermaLink="true">https://github.com/drkostas/high-sql</guid><description>A high-level SQL command utility. Currently only MySQL is supported.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Tools</category><category>PyPi</category><category>MySQL</category><category>CircleCI</category><category>wrapper</category></item><item><title>[Project] Cloud File Manager</title><link>https://github.com/drkostas/cloud-filemanager</link><guid isPermaLink="true">https://github.com/drkostas/cloud-filemanager</guid><description>A high-level filemanager utility for cloud services. Currently only Dropbox is supported.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Tools</category><category>PyPi</category><category>Dropbox</category><category>CircleCI</category><category>wrapper</category></item><item><title>[Project] YAML Wrapper</title><link>https://github.com/drkostas/yaml-config-wrapper</link><guid isPermaLink="true">https://github.com/drkostas/yaml-config-wrapper</guid><description>A YAML configuration wrapper.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Tools</category><category>PyPi</category><category>CircleCI</category><category>yaml</category><category>configuration</category><category>wrapper</category></item><item><title>[Project] Color Logger</title><link>https://github.com/drkostas/termcolor-logger</link><guid isPermaLink="true">https://github.com/drkostas/termcolor-logger</guid><description>A logger with text formatting using termcolor.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Tools</category><category>PyPi</category><category>CircleCI</category><category>logger</category><category>termcolor</category></item><item><title>[Project] Email Sender</title><link>https://github.com/drkostas/pyemail-sender</link><guid isPermaLink="true">https://github.com/drkostas/pyemail-sender</guid><description>A utility for sending emails with attachments. Currently only Gmail is supported.</description><pubDate>Wed, 15 Apr 2026 12:00:00 GMT</pubDate><category>Tools</category><category>PyPi</category><category>Gmail</category><category>wrapper</category></item><item><title>[Paper] Trustworthy AI for Early Dementia Detection: Robust Feature Masking and Clinical Interpretability</title><link>https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=b___QQ8AAAAJ&amp;citation_for_view=b___QQ8AAAAJ:Tyk-4Ss8FVUC</link><guid isPermaLink="true">https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=b___QQ8AAAAJ&amp;citation_for_view=b___QQ8AAAAJ:Tyk-4Ss8FVUC</guid><description>This work presents a trustworthy AI approach for early dementia detection, focusing on robust feature masking techniques and clinical interpretability to ensure reliable and transparent diagnostic support systems.</description><pubDate>Wed, 31 Dec 2025 23:59:59 GMT</pubDate><category>IEEE/ACM CHASE 2025</category></item><item><title>[Paper] Adding a teaching &quot;assistant&quot;: improving the quality of pseudo-labels for semi-supervised object detection</title><link>https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=b___QQ8AAAAJ&amp;citation_for_view=b___QQ8AAAAJ:UeHWp8X0CEIC</link><guid isPermaLink="true">https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=b___QQ8AAAAJ&amp;citation_for_view=b___QQ8AAAAJ:UeHWp8X0CEIC</guid><description>This paper introduces a novel approach to semi-supervised object detection by incorporating a teaching assistant mechanism to improve the quality of pseudo-labels, enhancing overall model performance and reliability.</description><pubDate>Wed, 31 Dec 2025 23:59:59 GMT</pubDate><category>Revista Tecnología en Marcha</category></item><item><title>[Paper] Koopman-Based Transition Detection in Satellite Imagery: Unveiling Construction Phase Dynamics Through Material Histogram Analysis</title><link>https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=b___QQ8AAAAJ&amp;sortby=pubdate&amp;authuser=3&amp;citation_for_view=b___QQ8AAAAJ:qjMakFHDy7sC</link><guid isPermaLink="true">https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=b___QQ8AAAAJ&amp;sortby=pubdate&amp;authuser=3&amp;citation_for_view=b___QQ8AAAAJ:qjMakFHDy7sC</guid><description>We reformulate construction phase classification as a transition detection problem and introduce a Koopman-Based Transition Detection (KTD) method that applies Koopman operator theory to analyze the nonlinear dynamics of material histograms in a linear framework. KTD employs a sliding window with Dynamic Mode Decomposition (DMD) on time-series material histograms and detects transition points by analyzing eigenvalue movement. Compared to CNN-based methods, KTD demonstrates enhanced accuracy and reduced temporal error.</description><pubDate>Tue, 31 Dec 2024 23:59:59 GMT</pubDate><category>IEEE/IGARSS 2024</category></item><item><title>[Paper] Advancing Multi-Scale Remote Sensing Analysis Through Self-Supervised Learning Fine-Tuning Strategies</title><link>https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=b___QQ8AAAAJ&amp;sortby=pubdate&amp;authuser=3&amp;citation_for_view=b___QQ8AAAAJ:2osOgNQ5qMEC</link><guid isPermaLink="true">https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=b___QQ8AAAAJ&amp;sortby=pubdate&amp;authuser=3&amp;citation_for_view=b___QQ8AAAAJ:2osOgNQ5qMEC</guid><description>This research focuses on improving the fine-tuning process of self-supervised learning models for remote sensing, particularly the Cross-Scale Masked Auto-Encoder (MAE). We tackle the challenges of intricate, multi-source imagery and present advancements in adapting the Cross-Scale MAE for diverse remote sensing environments.</description><pubDate>Tue, 31 Dec 2024 23:59:59 GMT</pubDate><category>IEEE/IGARSS 2024</category></item><item><title>[Paper] Ocassionally Secure: A Comparative Analysis of Code Generation Assistants</title><link>https://arxiv.org/abs/2402.00689</link><guid isPermaLink="true">https://arxiv.org/abs/2402.00689</guid><description>We conduct a comparative analysis of four advanced LLMs (GPT-3.5, GPT-4, Bard, Gemini) across 9 tasks to assess code generation capabilities. We focus on identifying conditions under which LLMs can be effectively and safely deployed for code generation, with emphasis on security awareness via two distinct developer personas. We collected 61 code outputs and analyzed them across functionality, security, performance, complexity, and reliability.</description><pubDate>Tue, 31 Dec 2024 23:59:59 GMT</pubDate><category>arXiv preprint</category></item><item><title>[Paper] Cross-Scale MAE: A Tale of Multiscale Exploitation in Remote Sensing</title><link>https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=b___QQ8AAAAJ&amp;sortby=pubdate&amp;authuser=1&amp;citation_for_view=b___QQ8AAAAJ:d1gkVwhDpl0C</link><guid isPermaLink="true">https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=b___QQ8AAAAJ&amp;sortby=pubdate&amp;authuser=1&amp;citation_for_view=b___QQ8AAAAJ:d1gkVwhDpl0C</guid><description>We present Cross-Scale MAE, a self-supervised model built on the Masked Auto-Encoder (MAE) framework for remote sensing image understanding. Cross-Scale MAE employs scale augmentation and enforces cross-scale consistency through both contrastive and generative losses to ensure consistent and meaningful representations for downstream tasks. Experimental evaluations demonstrate superior performance compared to standard MAE and other state-of-the-art remote sensing MAE methods.</description><pubDate>Sun, 31 Dec 2023 23:59:59 GMT</pubDate><category>NeurIPS 2023</category></item><item><title>[Paper] Semantic segmentation in aerial imagery using multi-level contrastive learning with local consistency</title><link>https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=b___QQ8AAAAJ&amp;sortby=pubdate&amp;authuser=1&amp;citation_for_view=b___QQ8AAAAJ:u-x6o8ySG0sC</link><guid isPermaLink="true">https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=b___QQ8AAAAJ&amp;sortby=pubdate&amp;authuser=1&amp;citation_for_view=b___QQ8AAAAJ:u-x6o8ySG0sC</guid><description>We exploit self-supervised contrastive learning for semantic segmentation in aerial imagery. In addition to feature-level CL, we add another level of contrastive learning at the semantic level, taking advantage of segmentation output. We embed local mutual information in the semantic-level CL to enforce local consistency, enhancing representation power and generalization. The proposed multi-level contrastive learning with local consistency (mCL-LC) shows superior performance and better generalization, especially under domain shift.</description><pubDate>Sun, 31 Dec 2023 23:59:59 GMT</pubDate><category>IEEE/WACV 2023</category></item><item><title>[Paper] A distributed hybrid community detection methodology for social networks</title><link>https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=b___QQ8AAAAJ&amp;sortby=pubdate&amp;authuser=1&amp;citation_for_view=b___QQ8AAAAJ:u5HHmVD_uO8C</link><guid isPermaLink="true">https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=b___QQ8AAAAJ&amp;sortby=pubdate&amp;authuser=1&amp;citation_for_view=b___QQ8AAAAJ:u5HHmVD_uO8C</guid><description>We combine network topology properties (loose similarity and local edge betweenness, an alternative to Girvan-Newman&apos;s edge betweenness) with intrinsic user content information to introduce a novel and highly distributed hybrid community detection methodology. The proposed approach is tested on real social graphs and compared to classic divisive community detection algorithms, proving exceptionally scalable, highly efficient, and accurate in revealing the subjacent network hierarchy.</description><pubDate>Tue, 31 Dec 2019 23:59:59 GMT</pubDate><category>Algorithms (MDPI) 2019</category></item></channel></rss>