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 …

Visual representation learning is ubiquitous in various real-world applications, including
visual comprehension, video understanding, multi-modal analysis, human-computer …

Abstract Video Question Answering (VideoQA) is the task of answering questions about a
video. At its core is understanding the alignments between visual scenes in video and …

Recent VQA models may tend to rely on language bias as a shortcut and thus fail to
sufficiently learn the multi-modal knowledge from both vision and language. In this paper …

Existing visual question answering methods often suffer from cross-modal spurious
correlations and oversimplified event-level reasoning processes that fail to capture event …

Video grounding aims to localize a moment from an untrimmed video for a given textual
query. Existing approaches focus more on the alignment of visual and language stimuli with …

Graph anomaly detection (GAD) is a challenging binary classification problem due to its
different structural distribution between anomalies and normal nodes---abnormal nodes are …

Cross-modal retrieval methods are the preferred tool to search databases for the text that
best matches a query image and vice versa However, image-text retrieval models commonly …

Weakly-supervised vision-language grounding aims to localize a target moment in a video
or a specific region in an image according to the given sentence query, where only video …

In the task of Visual Question Answering (VQA), most state-of-the-art models tend to learn
spurious correlations in the training set and achieve poor performance in out-of-distribution …