Humans learn powerful representations of objects and scenes by observing how they evolve
over time. Yet, outside of specific tasks that require explicit temporal understanding, static …

This paper introduces the Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for
LiDAR-based self-supervised pre-training and a carefully designed data-efficient 3D object …

Deep learning has achieved great success in learning features from massive remote
sensing images (RSIs). To better understand the connection between three feature learning …

In this paper, we efficiently transfer the surpassing representation power of the vision
foundation models, such as ViT and Swin, for video understanding with only a few trainable …

Representation learning stands as one of the critical machine learning techniques across
various domains. Through the acquisition of high-quality features, pre-trained embeddings …

The emergence of artificial emotional intelligence technology is revolutionizing the fields of
computers and robotics, allowing for a new level of communication and understanding of …

Humans excel at learning from expert demonstrations and solving their own problems. To
equip intelligent robots and assistants, such as AR glasses, with this ability, it is essential to …

How to learn discriminative video representation from unlabeled videos is challenging but
crucial for video analysis. The latest attempts seek to learn a representation model by …

Dynamic facial expression recognition (DFER) is essential to the development of intelligent
and empathetic machines. Prior efforts in this field mainly fall into supervised learning …

In contrast to numerous NLP and 2D computer vision foundational models, the learning of a
robust and highly generalized 3D foundational model poses considerably greater …