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Found 17 papers, 4 papers with code
An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions
Computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requirements.
An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry
We interviewed 12 practitioners from the most popular PTM ecosystem, Hugging Face, to learn the practices and challenges of PTM reuse.
Why Accuracy Is Not Enough: The Need for Consistency in Object Detection
Using this method, we show that the consistency of modern object detectors ranges from 83. 2% to 97. 1% on different video datasets from the Multiple Object Tracking Challenge.
Irrelevant Pixels are Everywhere: Find and Exclude Them for More Efficient Computer Vision
We observe that, given a computer vision task, images often contain pixels that are irrelevant to the task.
Efficient Computer Vision on Edge Devices with Pipeline-Parallel Hierarchical Neural Networks
We design a novel method that creates a parallel inference pipeline for computer vision problems that use hierarchical DNNs.
An Experience Report on Machine Learning Reproducibility: Guidance for Practitioners and TensorFlow Model Garden Contributors
To promote best practices within the engineering community, academic institutions and Google have partnered to launch a Special Interest Group on Machine Learning Models (SIGMODELS) whose goal is to develop exemplary implementations of prominent machine learning models in community locations such as the TensorFlow Model Garden (TFMG).
Low-Power Multi-Camera Object Re-Identification using Hierarchical Neural Networks
At each node in the hierarchy, a small DNN identifies a different attribute of the query image.
Automated Discovery of Real-Time Network Camera Data From Heterogeneous Web Pages
Reduction in the cost of Network Cameras along with a rise in connectivity enables entities all around the world to deploy vast arrays of camera networks.
Analyzing Worldwide Social Distancing through Large-Scale Computer Vision
This research team has created methods that can discover thousands of network cameras worldwide, retrieve data from the cameras, analyze the data, and report the sizes of crowds as different countries issued and lifted restrictions (also called ''lockdown'').
Low-Power Object Counting with Hierarchical Neural Networks
Object counting takes two inputs: an image and an object query and reports the number of occurrences of the queried object.
A Survey of Methods for Low-Power Deep Learning and Computer Vision
Deep neural networks (DNNs) are successful in many computer vision tasks.
Low-Power Computer Vision: Status, Challenges, Opportunities
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
See the World through Network Cameras
This paper describes the real-time data available from worldwide network cameras and potential applications.
Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution
The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems with low latency and power.
Cross-referencing Social Media and Public Surveillance Camera Data for Disaster Response
Physical media (like surveillance cameras) and social media (like Instagram and Twitter) may both be useful in attaining on-the-ground information during an emergency or disaster situation.
Large-Scale Object Detection of Images from Network Cameras in Variable Ambient Lighting Conditions
However, the images found in those datasets, are independent of one another and cannot be used to test YOLO's consistency at detecting the same object as its environment (e. g. ambient lighting) changes.
2018 Low-Power Image Recognition Challenge
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
