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SynBalance: balancing rare classes with synthetic data
PAPER ANALYSIS An ICCV 2025 paper uses generated images to repair long-tailed class imbalance — and gets real gains on the classes models usually ignore. It also walks straight into the limitation that matters most to us: diffusion generates plausible images, not specified ones. Computer vision models tend to learn a lot about what they…
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Synthetic Data in Computer Vision: From Scientific Revolution to Industrial Applications
The Revolution That Started in the Lab Over the past decades, we have witnessed a quiet yet powerful transformation in computer vision: synthetic data has shifted from an experimental resource to the backbone of cutting-edge commercial applications. What began as academic research now powers everything from autonomous vehicles to industrial safety systems. Digital Humans: The…
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ROSE: Object Removal in Videos Powered by Synthetic 3D Data
Generative models have made impressive progress in video editing and manipulation, but there’s still one very hard challenge: completely removing objects — not only the object itself, but also the side effects it creates such as shadows, reflections, illumination changes, translucency, and even mirror appearances. The recent work ROSE (Remove Objects with Side Effects in…
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DAViD by Microsoft: A Public Milestone in Computer Vision with Synthetic Data
When it comes to computer vision and synthetic data, we often see closed-off research — applied to proprietary contexts and far from public access. That’s why the recent DAViD project (Data-efficient and Accurate Vision models from synthetic Data), presented by Microsoft at ICCV 2025, stands out: it was fully released to the public, including code,…
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Is AI “shutting down” our brains? A reflection beyond the hype
Over the past few days, a study from MIT gained traction across social media. Dramatic interpretations quickly followed: that using ChatGPT was “shutting down” parts of the brain, that we’re losing our ability to think, that we’re becoming avatars on autopilot. Posts shared colorful images and catchy headlines — often without explaining what the study…
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The Collapse of “Ghost AI”: What Builder.ai and Amazon Teach Us – and Why SynthVision Is Different
When pretending to use AI is easier than actually building AI Recently, cases like Builder.ai — a UK-based startup backed by tech giants like Microsoft — reignited a crucial discussion about transparency in AI. The company, once valued at over $1 billion, promised to use artificial intelligence to create custom apps. In practice, however, it…
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How Synthetic Data Is Powering 4D Scene Reconstruction: Lessons from the Geo4D Paper
4D scene reconstruction — building 3D environments that evolve over time — is one of the most ambitious challenges in computer vision today. Traditionally, it requires large amounts of high-quality, annotated real-world data, which is expensive, time-consuming, and often impractical to collect. But what if we could train high-performing models without a single real-world sample?…
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The Future of AI Lies in Synthetic Data – And Tech Giants Already Know It
Artificial intelligence has advanced at an impressive pace in recent years, but a fundamental challenge is becoming increasingly evident: the scarcity of high-quality real data for training models. Major companies like Nvidia, Google, and OpenAI are now betting on synthetic data as a solution to overcome this limitation and keep AI progress moving forward. Nvidia’s…
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Blog: synthetic data and computer vision
De experimentos a insights — histórias reais sobre dados sintéticos e IA.From experiments to insights — real stories on synthetic data and AI.
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Posts recentesRecent Posts
- SynBalance: balancing rare classes with synthetic data
- Synthetic Data in Computer Vision: From Scientific Revolution to Industrial Applications
- ROSE: Object Removal in Videos Powered by Synthetic 3D Data
- DAViD by Microsoft: A Public Milestone in Computer Vision with Synthetic Data
- Is AI “shutting down” our brains? A reflection beyond the hype
