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

Knowledge distillation (KD) is a prevalent model compression technique in deep learning,
aiming to leverage knowledge from a large teacher model to enhance the training of a …

We propose a novel model named Multi-Channel Attention Selection Generative Adversarial
Network (SelectionGAN) for guided image-to-image translation, where we translate an input …

With the rapid development of wearable cameras, it is now feasible to considerably increase
the collection of egocentric video for first-person visual perception. However, the …

Continual learning is a research field of artificial neural networks to simulate human lifelong
learning ability. Although a surge of investigations has achieved considerable performance …

In recent years, open-vocabulary (OV) dense visual prediction (such as OV object detection,
semantic, instance and panoptic segmentations) has attracted increasing research attention …

Multi-Label Continual Learning (MLCL) is a framework designed for class-incremental multi-
label image recognition. However, MLCL faces two critical challenges: the construction of …

Continual learning, also known as incremental learning or life-long learning, stands at the
forefront of deep learning and AI systems. It breaks through the obstacle of one-way training …

Semantic Scene Completion (SSC) aims to reconstruct complete 3D scenes with precise
voxel-wise semantics from the single-view incomplete input data, a crucial but highly …

Open-world recognition has recently gained significant attention owing to its ability to bridge
the gap between experimental scenarios and real-world applications. Since continual …