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Found 12 papers, 7 papers with code
SqueezeNAS: Fast neural architecture search for faster semantic segmentation
For real time applications utilizing Deep Neural Networks (DNNs), it is critical that the models achieve high-accuracy on the target task and low-latency inference on the target computing platform.
Ranked #55 on
Semantic Segmentation
on Cityscapes val
(using extra training data)
DSCnet: Replicating Lidar Point Clouds with Deep Sensor Cloning
Recently, autonomous vehicles have created a demand for depth information, which is often obtained using hardware sensors such as Light detection and ranging (LIDAR).
Keynote: Small Neural Nets Are Beautiful: Enabling Embedded Systems with Small Deep-Neural-Network Architectures
Over the last five years Deep Neural Nets have offered more accurate solutions to many problems in speech recognition, and computer vision, and these solutions have surpassed a threshold of acceptability for many applications.
Exploring the Design Space of Deep Convolutional Neural Networks at Large Scale
Exploring the design space of CNN architectures.
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires real-time inference speed to guarantee prompt vehicle control, as well as small model size and energy efficiency to enable embedded system deployment.
How to scale distributed deep learning?
Training time on large datasets for deep neural networks is the principal workflow bottleneck in a number of important applications of deep learning, such as object classification and detection in automatic driver assistance systems (ADAS).
Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms
Results are presented for a case study of targeting the Qualcomm Snapdragon 820 mobile computing platform for CNN deployment.
libHOG: Energy-Efficient Histogram of Oriented Gradient Computation
Histogram of Oriented Gradients (HOG) features are the underlying representation in automotive computer vision applications such as collision avoidance and lane keeping.
From Captions to Visual Concepts and Back
The language model learns from a set of over 400, 000 image descriptions to capture the statistics of word usage.
Ranked #1 on
Image Captioning
on COCO Captions test
Deformable Part Models are Convolutional Neural Networks
Deformable part models (DPMs) and convolutional neural networks (CNNs) are two widely used tools for visual recognition.
Ranked #27 on
Object Detection
on PASCAL VOC 2007
DenseNet: Implementing Efficient ConvNet Descriptor Pyramids
Convolutional Neural Networks (CNNs) can provide accurate object classification.
Deformable Part Descriptors for Fine-grained Recognition and Attribute Prediction
Recognizing objects in fine-grained domains can be extremely challenging due to the subtle differences between subcategories.
Ranked #25 on
Fine-Grained Image Classification
on CUB-200-2011
