ai-research2.775 min read
UC Berkeley Researchers Use Large Language Models to Enhance Text-to-Image Synthesis.
2 years ago•Source: MarkTechPost
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3.89 min•2 years ago
Sketch-Based Object Detection Tool Revolutionizes Cancer Detection.
Researchers at the University of Surrey have developed a framework for object detection based on sketches, which can detect specific objects in a scene without requiring extra boundary boxes or class labels. The model is trained with a multi-category cross-entropy loss across the prototypes of all conceivable categories or instances in a weakly supervised object detection (WSOD) environment. The framework outperforms supervised and weakly supervised object detectors in a zero-shot setting and combines CLIP and Sketch-Based Image Retrieval (SBIR) to produce a sketch-aware detector that can function in a zero-shot fashion.
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"New AI Techniques for Image Compression and Visual Question Answering"
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Advancements in AI Language Models for Global Collaboration and Numerical Reasoning.
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Debunking the Myth of Scary AI Emergent Abilities: Insights from Stanford and John Hopkins Studies
Researchers from Stanford present an alternative explanation for the seemingly sharp and unpredictable emergent abilities of large language models (LLMs). They argue that the researcher's choice of a metric that nonlinearly or discontinuously deforms per-token error rates, the lack of test data to accurately estimate the performance of smaller models, and the evaluation of too few large-scale models are all causes of emergent abilities being a mirage. They provide a mathematical model to express their alternate viewpoint and show how it statistically supports the evidence for emergent LLM skills. They put their alternate theory to the test in three complementary ways and demonstrate that emergent skills only occur for certain metrics and not for model families on tasks.