Institute of Automation

Squeeze-and-Excitation Networks

The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial a...

Focal and efficient IOU loss for accurate bounding box regression

Fast Online Object Tracking and Segmentation: A Unifying Approach

In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach. Our method, du...

Session-Based Recommendation with Graph Neural Networks

The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representa...

A Light CNN for Deep Face Representation With Noisy Labels

The volume of convolutional neural network (CNN) models proposed for face recognition has been continuously growing larger to better fit the large amount of training data. When ...

Speech-Transformer: A No-Recurrence Sequence-to-Sequence Model for Speech Recognition

Recurrent sequence-to-sequence models using encoder-decoder architecture have made great progress in speech recognition task. However, they suffer from the drawback of slow trai...