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Found 7 papers, 1 papers with code
Safety Concerns and Mitigation Approaches Regarding the Use of Deep Learning in Safety-Critical Perception Tasks
Deep learning methods are widely regarded as indispensable when it comes to designing perception pipelines for autonomous agents such as robots, drones or automated vehicles.
Exploring Architectures for CNN-Based Word Spotting
The goal in word spotting is to retrieve parts of document images which are relevant with respect to a certain user-defined query.
Weakly Supervised Object Detection with Pointwise Mutual Information
It is shown that the combination of pointwise mutual information and a cosine loss eases the learning process and thus improves the accuracy.
Attribute CNNs for Word Spotting in Handwritten Documents
By taking a probabilistic perspective on training CNNs, we derive two different loss functions for binary and real-valued word string embeddings.
Learning Deep Representations for Word Spotting Under Weak Supervision
Convolutional Neural Networks have made their mark in various fields of computer vision in recent years.
Optimistic and Pessimistic Neural Networks for Scene and Object Recognition
A novel method for adjusting the network's predictions based on uncertainty information is introduced.
PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents
In recent years, deep convolutional neural networks have achieved state of the art performance in various computer vision task such as classification, detection or segmentation.
General Classification
Word Spotting In Handwritten Documents
