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Recent advances in multimodal foundation models like GPT-4V have shown strong performance in general visual and textual data tasks. However, adapting these models to specialized domains…
Text-to-image (T2I) models have seen rapid progress in recent years, allowing the generation of complex images based on natural language inputs. However, even state-of-the-art T2I models…
Multi-View and Multi-Scale Alignment for Mammography Contrastive Learning:Contrastive Language-Image Pre-training (CLIP) has shown potential in medical imaging, but its application to mammography faces challenges due to…
Large language and vision models (LLVMs) face a critical challenge in balancing performance improvements with computational efficiency. As models grow in size, reaching up to 80B…
The 3D occupancy prediction methods faced challenges in depth estimation, computational efficiency, and temporal information integration. Monocular vision struggled with depth ambiguities, while stereo vision required…
With the introduction of Large Language Models (LLMs), language creation has undergone a dramatic change, with a variety of language-related tasks being successfully integrated into a…
Monocular depth estimation (MDE) plays an important role in various applications, including image and video editing, scene reconstruction, novel view synthesis, and robotic navigation. However, this…
Accurately measuring physiological signals such as heart rate (HR) and heart rate variability (HRV) from facial videos using remote photoplethysmography (rPPG) presents several significant challenges. rPPG,…
Using advanced artificial intelligence models, video generation involves creating moving images from textual descriptions or static images. This area of research seeks to produce high-quality, realistic…
Previous 3D model generation from single images faced challenges. Feed-forward architectures produced simplistic objects due to limited 3D data. Gaussian splatting provided rapid coarse geometry but…