Scoping review finds large language models can support glaucoma education and decision support, but accuracy and multimodal ...

In recent years, foundation Vision-Language Models (VLMs), such as CLIP [1], which empower zero-shot transfer to a wide variety of domains without fine-tuning, have led to a significant shift in ...

Hugging Face Inc. today open-sourced SmolVLM-256M, a new vision language model with the lowest parameter count in its category. The algorithm’s small footprint allows it to run on devices such as ...

Deepseek VL-2 is a sophisticated vision-language model designed to address complex multimodal tasks with remarkable efficiency and precision. Built on a new mixture of experts (MoE) architecture, this ...

A research team has developed a chest X-ray vision-language foundation model, MaCo, reducing the dependency on annotations while improving both clinical efficiency and diagnostic accuracy. The study ...

MIT researchers discovered that vision-language models often fail to understand negation, ignoring words like “not” or “without.” This flaw can flip diagnoses or decisions, with models sometimes ...

Family of tunable vision-language models based on Gemma 2 generate long captions for images that describe actions, emotions, and narratives of the scene. Google has introduced a new family of ...

Imagine a world where your devices not only see but truly understand what they’re looking at—whether it’s reading a document, tracking where someone’s gaze lands, or answering questions about a video.

Crucially, these tests are generated by custom code and don’t rely on pre-existing images or tests that could be found on the public Internet, thereby “minimiz[ing] the chance that VLMs can solve by ...