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Found 30 papers, 17 papers with code
What Text Design Characterizes Book Genres?
In the experiment, we found that semantic information is sufficient to determine the genre; however, text design is helpful in adding more discriminative features for book genres.
Deep Attentive Time Warping
Unlike other learnable models using DTW for warping, our model predicts all local correspondences between two time series and is trained based on metric learning, which enables it to learn the optimal data-dependent warping for the target task.
Few shot font generation via transferring similarity guided global style and quantization local style
To better capture the local styles, a cross-attention-based style transfer module is adopted to transfer the styles of reference glyphs to the components, where the components are self-learned discrete latent codes through vector quantization without manual definition.
FETNet: Feature Erasing and Transferring Network for Scene Text Removal
To tackle these problems, we propose a novel Feature Erasing and Transferring (FET) mechanism to reconfigure the encoded features for STR in this paper.
Contour Completion by Transformers and Its Application to Vector Font Data
We propose a Transformer-based method to solve this problem and show the results of the typeface contour completion.
Vision Conformer: Incorporating Convolutions into Vision Transformer Layers
Transformers are popular neural network models that use layers of self-attention and fully-connected nodes with embedded tokens.
On Mini-Batch Training with Varying Length Time Series
We propose a novel method of normalizing the lengths of the time series in a dataset by exploiting the dynamic matching ability of Dynamic Time Warping (DTW).
Dynamic Data Augmentation with Gating Networks for Time Series Recognition
We propose a neural network that dynamically selects the best combination of data augmentation methods using a mutually beneficial gating network and a feature consistency loss.
Using Robust Regression to Find Font Usage Trends
In this paper, we attempt to specifically find the trends in font usage using robust regression on a large collection of text images.
Towards Book Cover Design via Layout Graphs
Book covers are intentionally designed and provide an introduction to a book.
Font Style that Fits an Image -- Font Generation Based on Image Context
We propose an end-to-end neural network that inputs the book cover, a target location mask, and a desired book title and outputs stylized text suitable for the cover.
Ranked #1 on
Font Generation
on Book Cover Dataset
Attention to Warp: Deep Metric Learning for Multivariate Time Series
This approach adapts a parameterized attention model to time warping for greater and more adaptive temporal invariance.
Self-Augmented Multi-Modal Feature Embedding
To exploit this fact, we propose the use of self-augmentation and combine it with multi-modal feature embedding.
What is the Reward for Handwriting? -- Handwriting Generation by Imitation Learning
In this study, we use a reinforcement learning (RL) framework to realize handwriting generation with the careful future planning ability.
An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks
In this paper, we survey data augmentation techniques for time series and their application to time series classification with neural networks.
Negative Pseudo Labeling using Class Proportion for Semantic Segmentation in Pathology
We propose a weakly-supervised cell tracking method that can train a convolutional neural network (CNN) by using only the annotation of "cell detection" (i. e., the coordinates of cell positions) without association information, in which cell positions can be easily obtained by nuclear staining.
On the Ability of a CNN to Realize Image-to-Image Language Conversion
The purpose of this paper is to reveal the ability that Convolutional Neural Networks (CNN) have on the novel task of image-to-image language conversion.
Guided neural style transfer for shape stylization
Designing logos, typefaces, and other decorated shapes can require professional skills.
Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teacher
In order to address this problem, we propose a novel time series data augmentation called guided warping.
Effect of Text Color on Word Embeddings
In natural scenes and documents, we can find the correlation between a text and its color.
Character-independent font identification
Moreover, we analyzed the relationship between character classes and font identification accuracy.
Neural Style Difference Transfer and Its Application to Font Generation
In our proposed method, the difference of font styles between two different fonts is found and transferred to another font using neural style transfer.
Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance Propagation
Through qualitative and quantitative analysis, we demonstrate that SGLRP can successfully localize and attribute the regions on input images which contribute to a target object's classification.
Modality Conversion of Handwritten Patterns by Cross Variational Autoencoders
This research attempts to construct a network that can convert online and offline handwritten characters to each other.
ProbAct: A Probabilistic Activation Function for Deep Neural Networks
We show that ProbAct increases the classification accuracy by +2-3% compared to ReLU or other conventional activation functions on both original datasets and when datasets are reduced to 50% and 25% of the original size.
How do Convolutional Neural Networks Learn Design?
We use LRP to explain the pixel-wise contributions of book cover design and highlight the design elements contributing towards particular genres.
Constrained Neural Style Transfer for Decorated Logo Generation
We propose using neural style transfer with clip art and text for the creation of new and genuine logos.
Dynamic Weight Alignment for Temporal Convolutional Neural Networks
In this paper, we propose a method of improving temporal Convolutional Neural Networks (CNN) by determining the optimal alignment of weights and inputs using dynamic programming.
Globally Optimal Object Tracking with Fully Convolutional Networks
Thus, we propose a new and robust tracking method using a Fully Convolutional Network (FCN) to obtain an object probability map and Dynamic Programming (DP) to seek the globally optimal path through all frames of video.
Judging a Book By its Cover
Book covers communicate information to potential readers, but can that same information be learned by computers?
Ranked #1 on
Genre classification
on Book Cover Dataset
