Deep Learning and its applications have cascaded impactful research and development
with a diverse range of modalities present in the real-world data. More recently, this has …

With the emergence of deep learning, computer vision has witnessed extensive
advancement and has seen immense applications in multiple domains. Specifically, image …

The NLP community has seen substantial recent interest in grounding to facilitate interaction
between language technologies and the world. However, as a community, we use the term …

Visual storytelling aims at generating a story from an image stream. Most existing methods
tend to represent images directly with the extracted high-level features, which is not intuitive …

Although recent developments in deep learning have brought several tasks closer to human
performance, there is still a significant gap between human and machine performance in …

Existing research for image captioning usually represents an image using a scene graph
with low-level facts (objects and relations) and fails to capture the high-level semantics. In …

Image–text retrieval is a vital task in computer vision and has received growing attention,
since it connects cross-modality data. It comes with the critical challenges of learning unified …

When answering questions about an image, it not only needs knowing what--understanding
the fine-grained contents (eg, objects, relationships) in the image, but also telling why …

Most image captioning models achieve superior performances with the help of large-scale
surprised training data, but it is prohibitively costly to label the image captions. To solve this …

Cross-modal retrieval has drawn much attention in recent years due to the diversity and the
quantity of information data that exploded with the popularity of mobile devices and social …