Automatic vision inspection holds significant importance in industry
inspection. While multimodal large language models (MLLMs) exhibit strong
language understanding capabilities and hold promise for this task, their
performance remains significant... read more
This review focuses on the structural organization of the hippocampus and how our understanding of its cellular architecture and functional circuits has been enabled over the last 400 years by the development of methods as varied as the Golgi impregn... read more
Empowering blind and low vision (BLV) users to explore visual media improves
content comprehension, strengthens user agency, and fulfills diverse
information needs. However, most existing tools separate exploration from the
main narration, which di... read more
Generating accurate, informative, and hallucination-free captions for charts
remains challenging for vision language models, primarily due to the lack of
large-scale, high-quality datasets of real-world charts. However, existing
real-world chart da... read more
In this paper, we propose theatre-in-the-loop, a framework for developing
expressive robot behaviours tailored to artistic performance through a
director-guided puppeteering workflow. Leveraging theatrical methods, we use
narrative objectives to di... read more
Journal of imaging informatics in medicine
Aug 5, 2025
Adequate withdrawal time is crucial in colonoscopy, as it is directly associated with polyp detection rates. However, traditional withdrawal time measurements can be biased by non-observation activities, leading to inaccurate assessments of procedura... read more
The rapid advancements in face forgery techniques necessitate that detectors
continuously adapt to new forgery methods, thus situating face forgery
detection within a continual learning paradigm. However, when detectors learn
new forgery types, the... read more
This study explores how artificial intelligence technologies can enhance the regulatory capacity for legal risks in internet healthcare based on a machine learning (ML) analytical framework and utilizes data from the health insurance portability and ... read more
Large language models with billions of parameters are often over-provisioned:
many layers contribute little unique information yet dominate the memory and
energy footprint during inference. We present LieQ, a metric-driven
post-training quantizatio... read more
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