SwAV: Swapping Assignments between Views
Simultaneously cluster the data and learn visual representations by enforcing consistency between cluster assignments, or ‘codes’, generated from different augmented views of the same image.
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Series & Collections
AI Fundamentals
AI Fundamentals: Lay a solid theoretical and methodological foundation for GeoAI learning.
Monte Carlo–Markov Chains Statistical Methods
Welcome to the Monte Carlo–Markov Chains Statistical Methods series, where we …
GeoAI Series
GeoAI = Geographic Information Science (GIS) + Artificial Intelligence (AI)
Remote Sensing with Python: A Hands-on Guide to Raster and Vector Data
📌 This series covers a complete workflow for handling remote sensing data in …
Latest Articles
MoCo: Momentum Contrast for Unsupervised Visual Representation Learning
It stabilizes and scales contrastive learning by maintaining a dynamic dictionary with momentum-based updates, becoming a cornerstone for modern SSL methods.
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Mastering TerraMind: From Understanding to Fine-tuning
TerraMind is the first large-scale, any-to-any generative multimodal foundation model proposed for the Earth Observation (EO) field. It is pre-trained by combining token-level and pixel-level dual-scale representations to learn high-level contextual information and fine-grained spatial details. The model aims to facilitate multimodal data integration, provide powerful generative capabilities, and support zero-shot and few-shot applications, while outperforming existing models on Earth Observation benchmarks and further improving performance by introducing ‘Thinking in Modalities’ (TiM).
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Monte Carlo Sampling
Understand the core concepts of Monte Carlo: Law of Large Numbers, rejection sampling, importance sampling, variance reduction techniques (antithetic variates, control variates, stratified sampling).
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Introduction to MCMC
The reason we need MCMC is that many distributions are only known in their unnormalized form, making traditional sampling/integration methods ineffective. By constructing a ‘correct Markov chain’, we can obtain the target distribution from its stationary distribution, meaning the long-term distribution of the trajectory ≈ target distribution.
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What is Probability?
This article introduces the basic concepts and rigorous formulas of probability, serving as the foundation for understanding random variables, sampling, and MCMC.
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Random Variables and Sampling
Understand concepts of random variables, PDF, expectation, and sampling methods for common distributions (Uniform, Normal, Exponential).
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Lesson 1: Introduction to Remote Sensing Data and Python Setup
Learn about raster and vector data and set up your Python environment
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title: “BYOL Explained: Self-Supervised Learning without Negative Pairs” date: 2025-10-08 summary: “Understanding BYOL: How interactions between Online and Target networks achieve SOTA performance without negative samples. A deep dive into the architecture and loss function.” series: [“Self-Supervised Learning”] tags: [“BYOL”, “Contrastive Learning”, “SSL”, “CV”, “Paper Notes”]
[Read More]title: “SimCLR Explained: Contrastive Learning Design & Code” date: 2025-10-07 summary: “A detailed visual guide to SimCLR. Understand the logic behind stochastic data augmentation, the NT-Xent loss, and why contrastive learning works.” series: [“Self-Supervised Learning”] tags: [“SimCLR”, “Contrastive Learning”, “SSL”, “CV”, “Paper Notes”]
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