activity
20182022
most citedSMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training

4 citations · 4 across the 2 of their papers we have counts for

collaborators

8 papers

cs.CV20224 cited

SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training

Yuanze Lin, Chen Wei, Huiyu Wang +2

Video-language pre-training is crucial for learning powerful multi-modal representation. However, it typically requires a massive amount of computation. In this paper, we develop S…

cs.CV2020

Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation

Huiyu Wang, Yukun Zhu, Bradley Green +3

Convolution exploits locality for efficiency at a cost of missing long range context. Self-attention has been adopted to augment CNNs with non-local interactions. Recent works prov…

cs.CV2019

Rethinking Normalization and Elimination Singularity in Neural Networks

Siyuan Qiao, Huiyu Wang, Chenxi Liu +2

In this paper, we study normalization methods for neural networks from the perspective of elimination singularity. Elimination singularities correspond to the points on the trainin…

cs.CV2019

Localizing Occluders with Compositional Convolutional Networks

Adam Kortylewski, Qing Liu, Huiyu Wang +2

Compositional convolutional networks are generative compositional models of neural network features, that achieve state of the art results when classifying partially occluded objec…

cs.CV2019

Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion

Adam Kortylewski, Qing Liu, Huiyu Wang +2

Deep convolutional neural networks (DCNNs) are powerful models that yield impressive results at object classification. However, recent work has shown that they do not generalize we…

cs.CV2019

Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval

Qing Liu, Lingxi Xie, Huiyu Wang +1

Sketch-based image retrieval (SBIR) is widely recognized as an important vision problem which implies a wide range of real-world applications. Recently, research interests arise in…