The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

In this paper, we propose a novel semi-supervised learning (SSL) framework named
BoostMIS that combines adaptive pseudo labeling and informative active annotation to …

Recent advancements in Multimodal Large Language Models (MLLMs) have been utilizing
Visual Prompt Generators (VPGs) to convert visual features into tokens that LLMs can …

Spatio-temporal video grounding aims to localize the aligned visual tube corresponding to a
language query. Existing techniques achieve such alignment by exploiting dense boundary …

Abstract Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a
new target domain by actively selecting a limited number of target data to annotate. This …

Recently, integrating vision and language for in-depth video understanding eg, video
captioning and video question answering, has become a promising direction for artificial …

Device Model Generalization (DMG) is a practical yet under-investigated research topic for
on-device machine learning applications. It aims to improve the generalization ability of pre …

Referring expression comprehension (REC) aims to localize a target object in an image
described by a referring expression phrased in natural language. Different from the object …

Image-text matching, as a fundamental cross-modal task, bridges the gap between vision
and language. The core is to accurately learn semantic alignment to find relevant shared …

Prompt tuning, a recently emerging paradigm, enables the powerful vision-language pre-
training models to adapt to downstream tasks in a parameter-and data-efficient way, by …