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Detecting Text in Natural Image with Connectionist Text Proposal Network
TLDR
A novel Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image and develops a vertical anchor mechanism that jointly predicts location and text/non-text score of each fixed-width proposal, considerably improving localization accuracy.
Adversarial Examples: Attacks and Defenses for Deep Learning
TLDR
The methods for generating adversarial examples for DNNs are summarized, a taxonomy of these methods is proposed, and three major challenges in adversarialExamples are discussed and the potential solutions are discussed.
Single Shot Text Detector with Regional Attention
TLDR
A novel single-shot text detector that directly outputs word-level bounding boxes in a natural image and develops a hierarchical inception module which efficiently aggregates multi-scale inception features.
Reading Scene Text in Deep Convolutional Sequences
TLDR
A deep recurrent model, building on long short-term memory (LSTM), is developed to robustly recognize the generated CNN sequences, departing from most existing approaches recognising each character independently.
Adaptive Adversarial Attack on Scene Text Recognition
TLDR
This work proposes an adaptive approach to speed up adversarial attacks, especially on sequential learning tasks, by leveraging the uncertainty of each task to directly learn the adaptive multi-task weightings, without manually searching hyper-parameters.
Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations
TLDR
This work introduces EfficientMORL, an efficient framework for the unsupervised learning of object-centric representation learning that demonstrates strong object decomposition and disentanglement on the standard multi-object benchmark while achieving nearly an order of magnitude faster training and test time inference over the previous state-of-theart model.
Intelligent Intersection: Two-Stream Convolutional Networks for Real-time Near Accident Detection in Traffic Video
TLDR
This paper proposes a two-stream Convolutional Network architecture that performs real-time detection, tracking, and near accident detection of road users in traffic video data and detects near accidents by incorporating appearance features and motion features from two- stream networks.
Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection
TLDR
This article introduces Propedeutica, a framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) techniques, and introduces a novel DL architecture (DeepMalware) for PropedeUTica with multistream inputs.
Intelligent Intersection
TLDR
This article proposes an integrated two-stream convolutional networks architecture that performs real-time detection, tracking, and near-accident detection of road users in traffic video data and detects near- Accident detection by incorporating appearance features and motion features from these two networks.
Boosting up Scene Text Detectors with Guided CNN
TLDR
This paper proposes a general framework for text detection called Guided CNN, which consists of one guidance subnetwork, where a guidance mask is learned from the input image itself, and one primary text detector, where every convolution and non-linear operation are conducted only in the guidance mask.
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