Blog(1)

• Mobile Twin Recognition

  Almost all of us has one or more smartphones, where we keep our personal data such as credit card information, photos, business contacts and so on. It is therefore important to protect this valuable information from unauthorized access.

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Research Achievements(56)

• Voice Spoofing Detection Through Residual Network, Max Feature Map, and Depthwise Separable Convolution

  AuthorIl-Youp Kwak; Sungsu Kwag; Junhee Lee; Youngbae Jeon; Jeonghwan Hwang; Hyo-Jung Choi; Jong-Hoon Yang; So-Yul Han, Jun Ho Huh, Choong-Hoon Lee, Ji Won Yoon

  PublishedIEEE Access

  Date2023-05-12

• LEARNING TO JOINTLY SHARE AND PRUNE WEIGHTS FOR GROUNDING BASED VISION AND LANGUAGE MODELS

  AuthorShangqian Gao,Burak Uzkent,Yilin Shen,Hongxia Jin

  PublishedInternational Conference on Learning Representation (ICLR)

  Date2023-05-01

• Efficient Flow-Guided Multi-frame De-fencing

  AuthorStavros Tsogkas, Fengjia Zhang, Allan Jepson, Alex Levinshtein

  PublishedIEEE Winter Conference on Applications of Computer Vision (WACV)

  Date2023-01-03

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News(4)

• [Blog] Task Generalizable Spatial and Texture Aware Image Downsizing Network

  Convolutional neural networks (CNNs) are widely used today in various vision tasks such as classification, detection and segmentation.

• [Blog] Dynamic Multi-scale Network for Dual-pixel Images Defocus Deblurring with Transformer

  Recent works achieve excellent results in defocus deblurring task based on dual-pixel data using convolutional neural network (CNN), while the scarcity of data limits the exploration and attempt of vision transformer in this task.

• [Blog] FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout

  Despite the soaring use of convolutional neural networks (CNNs) in mobile applications, uniformly sustaining high-performance inference on mobile has been elusive due to the excessive computational demands of modern CNNs and the increasing diversity of deployed devices. A popular alternative comprises offloading CNN processing to powerful cloud-based servers.

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