8 accepted papers
Image semantic segmentation is ubiquitously used in scene understanding applications, such as AI Camera, which require high accuracy and efficiency. Deep learning has significantly advanced the state-of-the-art in semantic segmentation. However, many of recent semantic segmentation works only consid…
Deep-leaming based acoustic echo cancellation (AEC) methods have been shown to outperform the classical techniques. The main drawback of the learning-based AEC is its dependency on the training set, which limits its practical deployment in mobile devices and unconstrained environments. This paper pr…
Monocular video depth estimation (MVDE) plays a crucial role in 3D computer vision. In this paper, we propose an end-to-end monocular video depth estimation network based on temporal attention. Our network starts by a motion compensation module where the spatial temporal transformer network (STN) is…
Transformer neural networks (TNN) demonstrated state-ofart performance on many natural language processing (NLP) tasks, replacing recurrent neural networks (RNNs), such as LSTMs or GRUs. However, TNNs did not perform well in speech enhancement, whose contextual nature is different than NLP tasks, li…
We consider the optimization of deep convolutional neural networks (CNNs) such that they provide good performance while having reduced complexity if deployed on either conventional systems with spatial-domain convolution or lower-complexity systems designed for Winograd convolution. The proposed fra…
In this paper, we propose a novel variable-rate learned image compression framework with a conditional autoencoder. Previous learning-based image compression methods mostly require training separate networks for different compression rates so they can yield compressed images of varying quality. In c…
Despite the remarkable progress achieved on automatic speech recognition, recognizing far-field speeches mixed with various noise sources is still a challenging task. In this paper, we introduce novel student-teacher transfer learning, BridgeNet which can provide a solution to improve distant speech…
Network quantization is one of network compression techniques to reduce the redundancy of deep neural networks. It reduces the number of distinct network parameter values by quantization in order to save the storage for them. In this paper, we design network quantization schemes that minimize the pe…