The field of computer vision is experiencing a great-leap-forward development today. This
paper aims at providing a comprehensive survey of the recent progress on computer vision …

Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a
wide range of cognitive tasks, and due to this, they have received significant interest from the …

We propose a network for Congested Scene Recognition called CSRNet to provide a data-
driven and deep learning method that can understand highly congested scenes and perform …

Building a high-performance FPGA accelerator for Deep Neural Networks (DNNs) often
requires RTL programming, hardware verification, and precise resource allocation, all of …

Deep neural networks have proven to be particularly effective in visual and audio
recognition tasks. Existing models tend to be computationally expensive and memory …

Quality inspection and defect detection remain critical challenges across diverse industrial
applications. Driven by advancements in Deep Learning, Convolutional Neural Networks …

Emerging edge computing platforms often contain machine learning (ML) accelerators that
can accelerate inference for a wide range of neural network (NN) models. These models are …

While embedded FPGAs are attractive platforms for DNN acceleration on edge-devices due
to their low latency and high energy efficiency, the scarcity of resources of edge-scale FPGA …

Reinforcement learning (RL) has attracted much attention recently, as new and emerging AI-
based applications are demanding the capabilities to intelligently react to environment …

Developing object detection and tracking on resource-constrained embedded systems is
challenging. While object detection is one of the most compute-intensive tasks from the …