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Motivation Autonomous vehicles rely on different sensors (cameras, LiDAR, and radar) each offering a unique view of the world. Combining them is key to reliable perception, but it’s tricky because cameras capture 2D images while LiDAR gives 3D structure. Earlier methods tried to force one
Let’s talk about DINOv2, a paper that takes a major leap forward in the quest for general-purpose visual features in computer vision. Inspired by the success of large-scale self-supervised models in NLP (think GPTs and T5), the authors at Meta have built a visual foundation
DeepSeek-V2 introduces a major architectural innovation that enhances its efficiency as a language model – Multi-Headed Latent Attention (MLA). MLA stands out as a game-changing technique that significantly reduces memory overhead while maintaining strong performance. In this post, we will explore the fundamental concepts behind
Scaling Supervision Instead of the Architecture Depth Anything doesn’t try to win monocular depth with a clever new backbone. It asks a simpler question: if you can cheaply manufacture supervision at Internet scale, can a very plain recipe become a foundation model? The authors pair
Today we’re going to dive into a fascinating paper called “LOGIC-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning.” The authors of this paper claim that Large Language Models (LLMs) often struggle when faced with complex logical reasoning problems. Well, LLMs primarily
Sagar Manglani
manglanisagar@gmail.com
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I write articles on computer vision and robotics.
