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VLMs have shown notable progress in perception-driven tasks such as visual question answering (VQA) and document-based visual reasoning. However, their effectiveness in reasoning-intensive tasks remains limited due to the scarcity of high-quality, diverse training datasets. Existing multimodal reasoning datasets have several shortcomings: some focus too narrowly on specific scientific imagery, others rely on synthetic data…
Machine learning has expanded beyond traditional Euclidean spaces in recent years, exploring representations in more complex geometric structures. Non-Euclidean representation learning is a growing field that seeks to capture the underlying geometric properties of data by embedding it in hyperbolic, spherical, or mixed-curvature product spaces. These approaches have been particularly useful in modeling hierarchical, structured,…
Optical Character Recognition (OCR) is a powerful technology that converts images of text into machine-readable content. With the growing need for automation in data extraction, OCR tools have become an essential part of many applications, from digitizing documents to extracting information from scanned images. In this tutorial, we will build an OCR app that runs…
Artificial Neural Networks (ANNs) have revolutionized computer vision with great performance, but their “black-box” nature creates significant challenges in domains requiring transparency, accountability, and regulatory compliance. The opacity of these systems hampers their adoption in critical applications where understanding decision-making processes is essential. Scientists are curious to understand these models’ internal mechanisms and want to…
Stereo depth estimation plays a crucial role in computer vision by allowing machines to infer depth from two images. This capability is vital for autonomous driving, robotics, and augmented reality applications. Despite advancements in deep learning, many existing stereo-matching models require domain-specific fine-tuning to achieve high accuracy. The challenge lies in developing a model that…
Modern VLMs struggle with tasks requiring complex visual reasoning, where understanding an image alone is insufficient, and deeper interpretation is needed. While recent advancements in LLMs have significantly improved text-based reasoning, similar progress in the visual domain remains limited. Existing VLMs often fail when required to combine visual and textual cues for logical deductions, highlighting…
LLMs are widely used for conversational AI, content generation, and enterprise automation. However, balancing performance with computational efficiency is a key challenge in this field. Many state-of-the-art models require extensive hardware resources, making them impractical for smaller enterprises. The demand for cost-effective AI solutions has led researchers to develop models that deliver high performance with…
Normalization layers have become fundamental components of modern neural networks, significantly improving optimization by stabilizing gradient flow, reducing sensitivity to weight initialization, and smoothing the loss landscape. Since the introduction of batch normalization in 2015, various normalization techniques have been developed for different architectures, with layer normalization (LN) becoming particularly dominant in Transformer models. Their…
In this tutorial, we demonstrate how to build an AI-powered PDF interaction system in Google Colab using Gemini Flash 1.5, PyMuPDF, and the Google Generative AI API. By leveraging these tools, we can seamlessly upload a PDF, extract its text, and interactively ask questions, receiving intelligent responses from Google’s latest Gemini Flash 1.5 model. Copy…
Like humans, large language models (LLMs) often have differing skills and strengths derived from differences in their architectures and training regimens. However, they struggle to combine specialized expertise across different domains, limiting their problem-solving capabilities compared to humans. Specialized models like MetaMath, WizardMath, and QwenMath excel at mathematical reasoning but often underperform on tasks requiring…