In the past year, MultiModal Large Language Models (MM-LLMs) have undergone
substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs …

Continual Named Entity Recognition (CNER) is a burgeoning area, which involves updating
an existing model by incorporating new entity types sequentially. Nevertheless, continual …

Incremental semantic segmentation focuses on continually learning the segmentation of
new coming classes without obtaining the training data from previously seen classes …

Incremental Learning (IL) has been a long-standing problem in both vision and Natural
Language Processing (NLP) communities. In recent years, as Pre-trained Language Models …

Abstract Continual Named Entity Recognition (CNER) is dedicated to sequentially learning
new entity types while mitigating catastrophic forgetting of old entity types. Traditional CNER …

The complementarity between camera and LiDAR data makes fusion methods a promising
approach to improve 3D semantic segmentation performance. Recent transformer-based …

Accurate segmentation of colorectal polyps in colonoscopy images is crucial for effective
diagnosis and management of colorectal cancer (CRC). However, current deep learning …

Local feature matching enjoys wide-ranging applications in the realm of computer vision,
encompassing domains such as image retrieval, 3D reconstruction, and object recognition …

Vision-language models (VLMs) have demonstrated remarkable open-vocabulary object
recognition capabilities, motivating their adaptation for dense prediction tasks like …

Continual named entity recognition requires models to be continuously updated to
recognize new entity types while retaining learned knowledge. In this task, the inherent …