Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

In this paper, we explore a novel model reusing task tailored for graph neural networks
(GNNs), termed as" deep graph reprogramming". We strive to reprogram a pre-trained GNN …

We revisit and advance visual prompting (VP), an input prompting technique for vision tasks.
VP can reprogram a fixed, pre-trained source model to accomplish downstream tasks in the …

Despite a surge of recent advances in promoting machine Learning (ML) fairness, the
existing mainstream approaches mostly require training or finetuning the entire weights of …

Chatbots shifted from rule-based to artificial intelligence techniques and gained traction in
medicine, shopping, customer services, food delivery, education, and research. OpenAI …

In this paper, we study the problem of temporal video grounding (TVG), which aims to predict
the starting/ending time points of moments described by a text sentence within a long …

In data-rich domains such as vision, language, and speech, deep learning prevails to deliver
high-performance task-specific models and can even learn general task-agnostic …

In this work, we leverage visual prompting (VP) to improve adversarial robustness of a fixed,
pre-trained model at test time. Compared to conventional adversarial defenses, VP allows …

Massive data is often considered essential for deep learning applications, but it also incurs
significant computational and infrastructural costs. Therefore, dataset pruning (DP) has …

Visual Speech Recognition (VSR) aims to infer speech into text depending on lip
movements alone. As it focuses on visual information to model the speech, its performance …