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 …

Class-incremental learning (CIL) has achieved remarkable successes in learning new
classes consecutively while overcoming catastrophic forgetting on old categories. However …

Abstract Class-Incremental Learning (CIL) requires a learning system to continually learn
new classes without forgetting. Despite the strong performance of Pre-Trained Models …

We study universal zero-shot segmentation in this work to achieve panoptic, instance, and
semantic segmentation for novel categories without any training samples. Such zero-shot …

Incremental Named Entity Recognition (INER) involves the sequential learning of new entity
types without accessing the training data of previously learned types. However, INER faces …

Continual learning algorithms which keep the parameters of new tasks close to that of
previous tasks, are popular in preventing catastrophic forgetting in sequential task learning …

Class-agnostic object counting aims to count all objects in an image with respect to example
boxes or class names aka few-shot and zero-shot counting. In this paper we propose a …

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 Named Entity Recognition (INER) aims to continually train a model with new
data, recognizing emerging entity types without forgetting previously learned ones. Prior …