Large models, encompassing large language and diffusion models, have shown
exceptional promise in approximating human-level intelligence, garnering significant …

Image segmentation is a long-standing challenge in computer vision, studied continuously
over several decades, as evidenced by seminal algorithms such as N-Cut, FCN, and …

In the medical field, the limited availability of large-scale datasets and labor-intensive
annotation processes hinder the performance of deep models. Diffusion-based generative …

Deep learning-based segmentation models have made remarkable progress in aiding
pulmonary disease diagnosis by segmenting lung lesion areas in large amounts of …

In recent years, significant progress has been made in collecting large-scale datasets to
improve segmentation and autonomous driving models. These large-scale datasets are …

Scalp diseases and alopecia affect millions of people around the world, underscoring the
urgent need for early diagnosis and management of the disease. However, the development …

The need for artificial intelligence (AI)-driven computer-assist ed diagnosis (CAD) tools
drives up the demand for large high-quality datasets in medical imaging. However …

Diffusion Model (DM) is a hot topic in deep generative models, and it is widely applied in the
image generation fields. In diffusion models, there are 4 main “secrets” that affect the …

The autonomous driving field has seen notable progress in segmentation and planning
model performance, driven by extensive datasets and innovative architectures. Yet, these …

Training current state-of-the-art models for object detection and segmentation requires a lot
of labeled data, which can be difficult to obtain. It is especially hard, when occurrence of an …