vision models
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Modular Pipelines vs End-to-End VLMs
Read Full Article: Modular Pipelines vs End-to-End VLMs![[D] Reasoning over images and videos: modular pipelines vs end-to-end VLMs](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-7835-300x171.png)
Exploring the best approach for reasoning over images and videos, the discussion contrasts modular pipelines with end-to-end Vision-Language Models (VLMs). While end-to-end VLMs show impressive capabilities, they often struggle with brittleness in complex tasks. A modular setup is proposed, where specialized vision models handle perception tasks like detection and tracking, and a Language Model (LLM) reasons over structured outputs. This approach aims to improve tasks such as event-based counting in traffic videos, tracking state changes, and grounding explanations to specific objects, while avoiding hallucinated references. The tradeoff between these methods is examined, questioning where modular pipelines excel and what reasoning tasks remain challenging for current video models. This matters because improving how machines interpret and reason over visual data can significantly enhance applications in areas like autonomous driving, surveillance, and multimedia analysis.
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PolyInfer: Unified Inference API for Vision Models
Read Full Article: PolyInfer: Unified Inference API for Vision Models
PolyInfer is a unified inference API designed to streamline the deployment of vision models across various hardware backends such as ONNX Runtime, TensorRT, OpenVINO, and IREE without the need to rewrite code for each platform. It simplifies dependency management and supports multiple devices, including CPUs, GPUs, and NPUs, by allowing users to install specific packages for NVIDIA, Intel, AMD, or all supported hardware. Users can load models, benchmark performance, and compare backend efficiencies with a single API, making it highly versatile for different machine learning tasks. The platform supports various operating systems and environments, including Windows, Linux, WSL2, and Google Colab, and is open-source under the Apache 2.0 license. This matters because it significantly reduces the complexity and effort required to deploy machine learning models across diverse hardware environments, enhancing accessibility and efficiency for developers.
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Optimizing Semiconductor Defect Classification with AI
Read Full Article: Optimizing Semiconductor Defect Classification with AI
Semiconductor manufacturing faces challenges in defect detection as devices become more complex, with traditional convolutional neural networks (CNNs) struggling due to high data requirements and limited adaptability. Generative AI, specifically NVIDIA's vision language models (VLMs) and vision foundation models (VFMs), offers a modern solution by leveraging advanced image understanding and self-supervised learning. These models reduce the need for extensive labeled datasets and frequent retraining, while enhancing accuracy and efficiency in defect classification. By integrating these AI-driven approaches, semiconductor fabs can improve yield, streamline processes, and reduce manual inspection efforts, paving the way for smarter and more productive manufacturing environments. This matters because it represents a significant leap in efficiency and accuracy for semiconductor manufacturing, crucial for the advancement of modern electronics.