Answering visual queries is a complex task that requires both visual processing and
reasoning. End-to-end models, the dominant approach for this task, do not explicitly …

Recently, DALL-E, a multimodal transformer language model, and its variants including
diffusion models have shown high-quality text-to-image generation capabilities. However …

Zero-shot learning (ZSL) aims to predict unseen classes whose samples have never
appeared during training. One of the most effective and widely used semantic information for …

The interaction decoder utilized in prevalent Transformer-based HOI detectors typically
accepts pre-composed human-object pairs as inputs. Though achieving remarkable …

Research on continual learning has recently led to a variety of work in unimodal community,
however little attention has been paid to multimodal tasks like visual question answering …

In recent years, several machine learning models have been proposed. They are trained
with a language modelling objective on large-scale text-only data. With such pretraining …

Computer vision systems today are primarily N-purpose systems, designed and trained for a
predefined set of tasks. Adapting such systems to new tasks is challenging and often …

Multimedia contents are of predominance in the modern Web era. Recent years have
witnessed growing research interests in multimedia recommendation, which aims to predict …

Abstract Machine learning has advanced dramatically, narrowing the accuracy gap to
humans in multimodal tasks like visual question answering (VQA). However, while humans …

Abstract General Purpose Vision (GPV) systems are models that are designed to solve a
wide array of visual tasks without requiring architectural changes. Today, GPVs primarily …