In recent years, Knowledge Graphs (KGs) have played a crucial role in the development of
advanced knowledge-intensive applications, such as recommender systems and semantic …

Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …

Multi-modal data abounds in biomedicine, such as radiology images and reports.
Interpreting this data at scale is essential for improving clinical care and accelerating clinical …

Despite the remarkable accuracy of deep neural networks in object detection, they are costly
to train and scale due to supervision requirements. Particularly, learning more object …

Recent years have witnessed the resurgence of knowledge engineering which is featured
by the fast growth of knowledge graphs. However, most of existing knowledge graphs are …

Abstract Existing Referring Image Segmentation (RIS) methods typically require expensive
pixel-level or box-level annotations for supervision. In this paper, we observe that the …

Phrase grounding, the problem of associating image regions to caption words, is a crucial
component of vision-language tasks. We show that phrase grounding can be learned by …

Vision-and-language models trained to match images with text can be combined with visual
explanation methods to point to the locations of specific objects in an image. Our work …

MRI brain image analysis, including brain tumor detection, is a challenging task. MRI images
are multimodal, and in recent years, multimodal medical image analysis has gotten more …

We consider the problem of segmenting image regions given a natural language phrase,
and study it on a novel dataset of 77,262 images and 345,486 phrase-region pairs. Our …