Influenced by the great success of deep learning via cloud computing and the rapid
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …

DNNs are becoming less and less over-parametrised due to recent advances in efficient
model design, through careful hand-crafted or NAS-based methods. Relying on the fact that …

Abstract We present Ego-Exo4D a diverse large-scale multimodal multiview video dataset
and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric …

We introduce A-ViT, a method that adaptively adjusts the inference cost of vision transformer
ViT for images of different complexity. A-ViT achieves this by automatically reducing the …

Built on top of self-attention mechanisms, vision transformers have demonstrated
remarkable performance on a variety of vision tasks recently. While achieving excellent …

Multimodal data collected from the real world are often imperfect due to missing modalities.
Therefore multimodal models that are robust against modal-incomplete data are highly …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude
of industries, including consumer electronics, healthcare, and manufacturing, largely due to …

Light-weight convolutional neural networks (CNNs) suffer performance degradation as their
low computational budgets constrain both the depth (number of convolution layers) and the …