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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
TLDR
This work addresses the task of semantic image segmentation with Deep Learning and proposes atrous spatial pyramid pooling (ASPP), which is proposed to robustly segment objects at multiple scales, and improves the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. Expand
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
TLDR
This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF). Expand
Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors
TLDR
A unified implementation of the Faster R-CNN, R-FCN and SSD systems is presented and the speed/accuracy trade-off curve created by using alternative feature extractors and varying other critical parameters such as image size within each of these meta-architectures is traced out. Expand
Progressive Neural Architecture Search
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionaryExpand
Deep Variational Information Bottleneck
TLDR
It is shown that models trained with the VIB objective outperform those that are trained with other forms of regularization, in terms of generalization performance and robustness to adversarial attack. Expand
Knowledge vault: a web-scale approach to probabilistic knowledge fusion
TLDR
The Knowledge Vault is a Web-scale probabilistic knowledge base that combines extractions from Web content (obtained via analysis of text, tabular data, page structure, and human annotations) with prior knowledge derived from existing knowledge repositories that computes calibrated probabilities of fact correctness. Expand
Generation and Comprehension of Unambiguous Object Descriptions
TLDR
This work proposes a method that can generate an unambiguous description of a specific object or region in an image and which can also comprehend or interpret such an expression to infer which object is being described, and shows that this method outperforms previous methods that generate descriptions of objects without taking into account other potentially ambiguous objects in the scene. Expand
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification
TLDR
It is shown that it is possible to replace many of the 3D convolutions by low-cost 2D convolution, suggesting that temporal representation learning on high-level “semantic” features is more useful. Expand
A Review of Relational Machine Learning for Knowledge Graphs
TLDR
This paper provides a review of how statistical models can be “trained” on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph) and how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. Expand
Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation
TLDR
Expectation-Maximization (EM) methods for semantic image segmentation model training under weakly supervised and semi-supervised settings are developed and extensive experimental evaluation shows that the proposed techniques can learn models delivering competitive results on the challenging PASCAL VOC 2012 image segmentsation benchmark, while requiring significantly less annotation effort. Expand
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