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AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling
In vision-enabled autonomous systems such as robots and autonomous cars,...
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Draw This Object: A Study of Debugging Representations
Domain-specific debugging visualizations try to provide a view of a runt...
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Dataset Culling: Towards Efficient Training Of Distillation-Based Domain Specific Models
Real-time CNN based object detection models for applications like survei...
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Training Domain Specific Models for Energy-Efficient Object Detection
We propose an end-to-end framework for training domain specific models (...
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ABOShips – An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations
Availability of domain-specific datasets is an essential problem in obje...
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Meta-Cognition-Based Simple And Effective Approach To Object Detection
Recently, many researchers have attempted to improve deep learning-based...
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Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection
A significant challenge in object detection is accurate identification o...
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Domain Specific Approximation for Object Detection
There is growing interest in object detection in advanced driver assistance systems and autonomous robots and vehicles. To enable such innovative systems, we need faster object detection. In this work, we investigate the trade-off between accuracy and speed with domain-specific approximations, i.e. category-aware image size scaling and proposals scaling, for two state-of-the-art deep learning-based object detection meta-architectures. We study the effectiveness of applying approximation both statically and dynamically to understand the potential and the applicability of them. By conducting experiments on the ImageNet VID dataset, we show that domain-specific approximation has great potential to improve the speed of the system without deteriorating the accuracy of object detectors, i.e. up to 7.5x speedup for dynamic domain-specific approximation. To this end, we present our insights toward harvesting domain-specific approximation as well as devise a proof-of-concept runtime, AutoFocus, that exploits dynamic domain-specific approximation.
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