5 accepted papers
While mainstream vision-language models (VLMs) have advanced rapidly in understanding image-level information, they still lack the ability to focus on specific areas designated by humans. Rather, they typically rely on large volumes of high-quality image-text paired data to learn and generate poster…
AI for tumor segmentation is limited by the lack of large, voxel-wise annotated datasets, which are hard to create and require medical experts. In our proprietary JHH dataset of 3,000 annotated pancreatic tumor scans, we found that AI performance stopped improving after 1,500 scans. With synthetic d…
Unsupervised domain adaptive semantic segmentation leverages synthetic data to train a segmentation model and transfers it to unlabeled real images. Due to the style difference, the transferred model suffers from the domain gap. Even worse, some classes exhibit the extreme domain gap, where the feat…
In partial label learning (PLL), each instance is associated with a set of candidate labels among which only one is ground-truth. The majority of the existing works focuses on constructing robust classifiers to estimate the labeling confidence of candidate labels in order to identify the correct one…
When handling complicated text images (e.g., irregular structures, low resolution, heavy occlusion, and uneven illumination), existing supervised text recognition methods are data-hungry. Although these methods employ large-scale synthetic text images to reduce the dependence on annotated real image…