Tiny machine learning (TinyML) is a new frontier of machine learning. By squeezing deep
learning models into billions of IoT devices and microcontrollers (MCUs), we expand the …

Tiny deep learning on microcontroller units (MCUs) is challenging due to the limited memory
size. We find that the memory bottleneck is due to the imbalanced memory distribution in …

Tracking humans in crowded video sequences is an important constituent of visual scene
understanding. Increasing crowd density challenges visibility of humans, limiting the …

Face detection has received intensive attention in recent years. Many works present lots of
special methods for face detection from different perspectives like model architecture, data …

Face detection is a crucial first step in many facial recognition and face analysis systems.
Early approaches for face detection were mainly based on classifiers built on top of hand …

Face detection has achieved significant progress in recent years. However, high
performance face detection still remains a very challenging problem, especially when there …

Abstract Standard Convolutional Neural Networks (CNNs) designed for computer vision
tasks tend to have large intermediate activation maps. These require large working memory …

With the widespread application of drone in commercial and industrial fields, drone
detection has received increasing attention in public safety and others. However, due to …

Ambient Intelligence (AmI) encompasses technological infrastructures capable of sensing
data from environments and extracting high-level knowledge to detect or recognize users' …

Designing a lightweight and robust portrait segmentation algorithm is an important task for a
wide range of face applications. However, the problem has been considered as a subset of …