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Found 10 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.
Restructurable Activation Networks
To address this question, we propose a new paradigm called Restructurable Activation Networks (RANs) that manipulate the amount of non-linearity in models to improve their hardware-awareness and efficiency.
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.
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.
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.
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.
