In the rapidly evolving landscape of artificial intelligence, multimodal learning systems
(MMLS) have gained traction for their ability to process and integrate information from …

Artificial neural networks often suffer from catastrophic forgetting where learning new
concepts leads to a complete loss of previously acquired knowledge. We observe that this …

Despite tremendous advancements, current state-of-the-art Vision-Language Models
(VLMs) are still far from perfect. They tend to hallucinate and may generate biased …

Vision-language models (VLMs) have emerged as formidable tools, showing their strong
capability in handling various open-vocabulary tasks in image recognition, text-driven visual …

Adapting a pre-trained foundation model on downstream tasks should ensure robustness
against distribution shifts without the need to retrain the whole model. Although existing …

While deep neural networks have achieved remarkable performance, they tend to lack
transparency in prediction. The pursuit of greater interpretability in neural networks often …

Transferring visual-language knowledge from large-scale foundation models for video
recognition has proved to be effective. To bridge the domain gap, additional parametric …

In the field of machine learning, continual learning is a crucial concept that allows models to
adapt to non-stationary data distributions. However, most of the existing works focus on uni …

Vision-language models (VLMs) have emerged as formidable tools, showing their strong
capability in handling various open-vocabulary tasks in image recognition, text-driven visual …

Meta-learning in the broader context concerns how an agent learns about their own
learning, allowing them to improve their learning process. Learning how to learn is not only …