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The strong generalization abilities of large-scale vision foundation models have contributed to their amazing performance in various computer vision tasks. These models are quite adaptable since…
Supervised learning in medical image classification faces challenges due to the scarcity of labeled data, as expert annotations are difficult to obtain. Vision-Language Models (VLMs) address…
Generative modeling, particularly diffusion models (DMs), has significantly advanced in recent years, playing a crucial role in generating high-quality images, videos, and audio. Diffusion models operate…
Reconstructing high-fidelity surfaces from multi-view images, especially with sparse inputs, is a critical challenge in computer vision. This task is essential for various applications, including autonomous…
Understanding multi-page documents and news videos is a common task in human daily life. To tackle such scenarios, Multimodal Large Language Models (MLLMs) should be equipped…
Recent advancements in medical multimodal large language models (MLLMs) have shown significant progress in medical decision-making. However, many models, such as Med-Flamingo and LLaVA-Med, are designed…
In computer vision, backbone architectures are critical in image recognition, object detection, and semantic segmentation tasks. These backbones extract local and global features from images, enabling…
Text-to-image diffusion models have made significant strides in generating complex and faithful images from input conditions. Among these, Diffusion Transformers Models (DiTs) have emerged as particularly…
Computer vision is rapidly transforming industries by enabling machines to interpret and make decisions based on visual data. From autonomous vehicles to medical imaging, its applications…
Adapting 2D-based segmentation models to effectively process and segment 3D data presents a significant challenge in the field of computer vision. Traditional approaches often struggle to…