Latest AI and machine learning research in ophthalmology for healthcare professionals.
Category-selective regions in the human brain, such as the fusiform face area (FFA), extrastriate body area (EBA), parahippocampal place area (PPA), and visual word form area (VWFA), play a crucial role in high-level visual processing. Here, we investigate whether artificial neural networks (ANNs) exhibit similar category-selective neurons and how these neurons vary across model layers and betwe...
The rapid success of Vision Large Language Models (VLLMs) often depends on the high-resolution images with abundant visual tokens, which hinders training and deployment efficiency. Current training-free visual token compression methods exhibit serious performance degradation in tasks involving high-resolution, text-oriented image understanding and reasoning. In this paper, we propose an efficien...
Recent multimodal foundation models are primarily trained on English or high resource European language data, which hinders their applicability to o...
Vision-Language Models (VLMs) occasionally generate outputs that contradict input images, constraining their reliability in real-world applications....
Multimodal language models (MLMs) integrate visual and textual information by coupling a vision encoder with a large language model through the spec...
Sparse Mixture-of-Experts (MoE) architectures have emerged as a promising approach to decoupling model capacity from computational cost. At the core...
Image Steganography is a cryptographic technique that embeds secret information into an image, ensuring the hidden data remains undetectable to the ...
Predicting seizure freedom is essential for tailoring epilepsy treatment. But accurate prediction remains challenging with traditional methods, espe...
Multimodal Large Language Models (MLLMs) have shown impressive performance in vision and text tasks. However, hallucination remains a major challeng...
Optical coherence tomography angiography (OCTA) is a non-invasive imaging technique widely used to study vascular structures and micro-circulation d...
We introduce SigLIP 2, a family of new multilingual vision-language encoders that build on the success of the original SigLIP. In this second iterat...
Efficient and privacy-preserving multimodal interaction is essential as AR, VR, and modern smartphones with powerful cameras become primary interfac...
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a range of multimodal tasks. However, their inference efficien...
Context: Accessibility issues (e.g., small size and narrow interval) in mobile applications (apps) lead to obstacles for billions of low vision user...
Recent advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have made them powerful tools in embodied navigation, enabling...
Mobile robots necessitate advanced natural language understanding capabilities to accurately identify locations and perform tasks such as package de...
2D image coding for machines (ICM) has achieved great success in coding efficiency, while less effort has been devoted to stereo image fields. To pr...
In the evolving landscape of multimodal language models, understanding the nuanced meanings conveyed through visual cues - such as satire, insult, o...
Vision foundation models (VFMs) are pre-trained on extensive image datasets to learn general representations for diverse types of data. These models...
Large Multimodal Models (LMMs), or Vision-Language Models (VLMs), have shown impressive capabilities in a wide range of visual tasks. However, they ...