Self-attention has the promise of improving computer vision systems due to parameter-
independent scaling of receptive fields and content-dependent interactions, in contrast to …

Recently, pure transformer-based models have shown great potentials for vision tasks such
as image classification and detection. However, the design of transformer networks is …

Vision Transformers convert images to sequences by slicing them into patches. The size of
these patches controls a speed/accuracy tradeoff, with smaller patches leading to higher …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

Neural Architecture Search (NAS) has been explosively studied to automate the discovery of
top-performer neural networks. Current works require heavy training of supernet or intensive …

One of the most critical problems in weight-sharing neural architecture search is the
evaluation of candidate models within a predefined search space. In practice, a one-shot …

Current dynamic networks and dynamic pruning methods have shown their promising
capability in reducing theoretical computation complexity. However, dynamic sparse …