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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
TLDR
This work introduces two simple global hyper-parameters that efficiently trade off between latency and accuracy and demonstrates the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
TLDR
This work extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries and applies the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules, resulting in a faster and stronger encoder-decoder network.
Rethinking Atrous Convolution for Semantic Image Segmentation
TLDR
The proposed `DeepLabv3' system significantly improves over the previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark.
Searching for MobileNetV3
TLDR
This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art of MobileNets.
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
TLDR
A quantization scheme is proposed that allows inference to be carried out using integer- only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware.
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
TLDR
This paper presents a network level search space that includes many popular designs, and develops a formulation that allows efficient gradient-based architecture search and demonstrates the effectiveness of the proposed method on the challenging Cityscapes, PASCAL VOC 2012, and ADE20K datasets.
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
TLDR
For the first time, a bottom-up approach could deliver state-of-the-art results on panoptic segmentation, and performs on par with several top-down approaches on the challenging COCO dataset.
FEELVOS: Fast End-To-End Embedding Learning for Video Object Segmentation
TLDR
This work proposes FEELVOS as a simple and fast method which does not rely on fine-tuning, and achieves a new state of the art in video object segmentation without fine- Tuning with a J&F measure of 71.5% on the DAVIS 2017 validation set.
The iNaturalist Species Classification and Detection Dataset
TLDR
The iNaturalist species classification and detection dataset, consisting of 859,000 images from over 5,000 different species of plants and animals, is presented, which features visually similar species, captured in a wide variety of situations, from all over the world.
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications
TLDR
An algorithm that automatically adapts a pre-trained deep neural network to a mobile platform given a resource budget while maximizing the accuracy, and achieves better accuracy versus latency trade-offs on both mobile CPU and mobile GPU, compared with the state-of-the-art automated network simplification algorithms.
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