Multimodal large language models are beginning to transform science education by combining text, visuals, audio, and other data to enrich teaching and learning. From analyzing classroom interactions ...

Enterprises are rethinking AI-assisted content creation by combining governance policies, multimodal integration, and human oversight to balance speed with credibility. New approaches span interactive ...

Alibaba's HDPO framework trains AI agents to skip unnecessary tool calls, cutting redundant invocations from 98% to 2% while ...

The launch of NVIDIA Nemotron 3 Nano Omni forces engineering teams to rethink multimodal AI deployment to maximise inference ...

ChatGPT Image 2.0 suggests that AI image generation is evolving into visual reasoning and verifiable AI, with implications ...

CLAM/ Data preprocessing and whole-slide tiling utilities based on CLAM [1]. Includes custom artifact removal using HSV color-based segmentation and tiling pipelines for WSI patch extraction. Example ...

A study on visual language models explores how shared semantic frameworks improve image–text understanding across multimodal tasks. By ...

Modality-agnostic decoders leverage modality-invariant representations in human subjects' brain activity to predict stimuli irrespective of their modality (image, text, mental imagery).

This study investigated how Chinese learners of English perceive the effectiveness of different multimodal input for vocabulary learning. Forty participants perceived 14 combinations of visual, ...

Abstract: Remote sensing (RS) image-text retrieval is challenging due to the inherent complexity of RS imagery and significant information imbalance between the image and text data. Existing CLIP ...