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 …

The trustworthiness of machine learning has emerged as a critical topic in the field,
encompassing various applications and research areas such as robustness, security …

The visual classification performance of vision-language models such as CLIP has been
shown to benefit from additional semantic knowledge from large language models (LLMs) …

Class-Incremental Learning (CIL) or continual learning is a desired capability in the real
world, which requires a learning system to adapt to new tasks without forgetting former ones …

Task driven object detection aims to detect object instances suitable for affording a task in an
image. Its challenge lies in object categories available for the task being too diverse to be …

Prompt learning has attracted broad attention in computer vision since the large pre-trained
vision-language models (VLMs) exploded. Based on the close relationship between vision …

Abstract Video Anomaly Detection (VAD) is crucial for applications such as security
surveillance and autonomous driving. However, existing VAD methods provide little …

Abstract Vision Language Models (VLMs) excel in zero-shot image classification by pairing
images with textual category names. The expanding variety of Pre-Trained VLMs enhances …

CLIP showcases exceptional cross-modal matching capabilities due to its training on image-
text contrastive learning tasks. However without specific optimization for unimodal scenarios …

Continual Learning (CL) enables machine learning models to learn from continuously
shifting new training data in absence of data from old tasks. Recently pre-trained vision …