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Found 26 papers, 12 papers with code
What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims
We successfully test 3 of these claims through a quantitative analysis, and directly compare one with traditional software.
From Attack to Defense: Insights into Deep Learning Security Measures in Black-Box Settings
Using various datasets such as ImageNet-1000, CIFAR-100, and CIFAR-10 are used to evaluate the black-box attacks.
2023 Low-Power Computer Vision Challenge (LPCVC) Summary
This article describes the 2023 IEEE Low-Power Computer Vision Challenge (LPCVC).
PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software
Our analysis of this dataset provides the first summary statistics for the PTM supply chain, showing the trend of PTM development and common shortcomings of PTM package documentation.
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.
PeaTMOSS: Mining Pre-Trained Models in Open-Source Software
Developing and training deep learning models is expensive, so software engineers have begun to reuse pre-trained deep learning models (PTMs) and fine-tune them for downstream tasks.
Naming Practices of Pre-Trained Models in Hugging Face
PTM authors should choose appropriate names for their PTMs, which would facilitate model discovery and reuse.
Single-Class Target-Specific Attack against Interpretable Deep Learning Systems
The universal perturbation is stochastically and iteratively optimized by minimizing the adversarial loss that is designed to consider both the classifier and interpreter costs in targeted and non-targeted categories.
Analysis of Failures and Risks in Deep Learning Model Converters: A Case Study in the ONNX Ecosystem
We find that the node conversion stage of a model converter accounts for ~75% of the defects and 33% of reported failure are related to semantically incorrect models.
Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision
We describe this process as deep learning model reengineering.
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.
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.
Auto-generated Spies Increase Test Maintainability
We have inspected the test code for the scala. collection. Iterator trait for potential systematic maintainability enhancements.
Software Engineering
