16 citations · 94 across the 59 of their papers we have counts for
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Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge
Zihao Ye, Yung-Hsiang Lu, Xiao Hu +14
The IEEE Low-Power Computer Vision Challenge (LPCVC) aims to promote the development of efficient vision models for edge devices, balancing accuracy with constraints such as latenc…
AdaPerceiver: Transformers with Adaptive Width, Depth, and Tokens
Purvish Jajal, Nick John Eliopoulos, Benjamin Shiue-Hal Chou +3
Modern transformer architectures achieve remarkable performance across tasks and domains but remain rigid in how they allocate computation at inference time. Real-world deployment…
Impact of Architectural Modifications on Deep Learning Adversarial Robustness
Firuz Juraev, Mohammed Abuhamad, Simon S. Woo +2
Rapid advancements of deep learning are accelerating adoption in a wide variety of applications, including safety-critical applications such as self-driving vehicles, drones, robot…
2023 Low-Power Computer Vision Challenge (LPCVC) Summary
Leo Chen, Benjamin Boardley, Ping Hu +27
This article describes the 2023 IEEE Low-Power Computer Vision Challenge (LPCVC). Since 2015, LPCVC has been an international competition devoted to tackling the challenge of compu…
An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions
Caleb Tung, Nicholas Eliopoulos, Purvish Jajal +7
Computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requireme…
Single-Class Target-Specific Attack against Interpretable Deep Learning Systems
Eldor Abdukhamidov, Mohammed Abuhamad, George K. Thiruvathukal +2
In this paper, we present a novel Single-class target-specific Adversarial attack called SingleADV. The goal of SingleADV is to generate a universal perturbation that deceives the…
Why Accuracy Is Not Enough: The Need for Consistency in Object Detection
Caleb Tung, Abhinav Goel, Fischer Bordwell +4
Object detectors are vital to many modern computer vision applications. However, even state-of-the-art object detectors are not perfect. On two images that look similar to human ey…
Irrelevant Pixels are Everywhere: Find and Exclude Them for More Efficient Computer Vision
Caleb Tung, Abhinav Goel, Xiao Hu +5
Computer vision is often performed using Convolutional Neural Networks (CNNs). CNNs are compute-intensive and challenging to deploy on power-contrained systems such as mobile and I…
Efficient Computer Vision on Edge Devices with Pipeline-Parallel Hierarchical Neural Networks
Abhinav Goel, Caleb Tung, Xiao Hu +3
Computer vision on low-power edge devices enables applications including search-and-rescue and security. State-of-the-art computer vision algorithms, such as Deep Neural Networks (…
Low-Power Multi-Camera Object Re-Identification using Hierarchical Neural Networks
Abhinav Goel, Caleb Tung, Xiao Hu +4
Low-power computer vision on embedded devices has many applications. This paper describes a low-power technique for the object re-identification (reID) problem: matching a query im…
Analyzing Worldwide Social Distancing through Large-Scale Computer Vision
Isha Ghodgaonkar, Subhankar Chakraborty, Vishnu Banna +15
In order to contain the COVID-19 pandemic, countries around the world have introduced social distancing guidelines as public health interventions to reduce the spread of the diseas…
Low-Power Object Counting with Hierarchical Neural Networks
Abhinav Goel, Caleb Tung, Sara Aghajanzadeh +4
Deep Neural Networks (DNNs) can achieve state-of-the-art accuracy in many computer vision tasks, such as object counting. Object counting takes two inputs: an image and an object q…
A Survey of Methods for Low-Power Deep Learning and Computer Vision
Abhinav Goel, Caleb Tung, Yung-Hsiang Lu +1
Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computa…
Low-Power Computer Vision: Status, Challenges, Opportunities
Sergei Alyamkin, Matthew Ardi, Alexander C. Berg +41
Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile…
See the World through Network Cameras
Yung-Hsiang Lu, George K. Thiruvathukal, Ahmed S. Kaseb +7
Millions of network cameras have been deployed worldwide. Real-time data from many network cameras can offer instant views of multiple locations with applications in public safety,…
Large-Scale Object Detection of Images from Network Cameras in Variable Ambient Lighting Conditions
Caleb Tung, Matthew R. Kelleher, Ryan J. Schlueter +5
Computer vision relies on labeled datasets for training and evaluation in detecting and recognizing objects. The popular computer vision program, YOLO ("You Only Look Once"), has b…
2018 Low-Power Image Recognition Challenge
Sergei Alyamkin, Matthew Ardi, Achille Brighton +38
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing.ieee.org/lpirc) is an annual competition started in 2015. The competition identifies the best technolog…