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Found 9 papers, 3 papers with code
GLASS: Global to Local Attention for Scene-Text Spotting
In recent years, the dominant paradigm for text spotting is to combine the tasks of text detection and recognition into a single end-to-end framework.
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Learning Multimodal Affinities for Textual Editing in Images
Nowadays, as cameras are rapidly adopted in our daily routine, images of documents are becoming both abundant and prevalent.
On Calibration of Scene-Text Recognition Models
Although the topic of confidence calibration has been an active research area for the last several decades, the case of structured and sequence prediction calibration has been scarcely explored.
Sequence-to-Sequence Contrastive Learning for Text Recognition
We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations, which we apply to text recognition.
SCATTER: Selective Context Attentional Scene Text Recognizer
The first attention step re-weights visual features from a CNN backbone together with contextual features computed by a BiLSTM layer.
Deep Randomized Least Squares Value Iteration
Rather than using hand-design state representation, we use a state representation that is being learned directly from the data by a DQN agent.
End-to-End Differentiable Adversarial Imitation Learning
Generative Adversarial Networks (GANs) have been successfully applied to the problem of policy imitation in a model-free setup.
Model-based Adversarial Imitation Learning
A model-based approach for the problem of adversarial imitation learning.
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning
Instability and variability of Deep Reinforcement Learning (DRL) algorithms tend to adversely affect their performance.
