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Found 7 papers, 3 papers with code
Total Disentanglement of Font Images into Style and Character Class Features
Second, it is demonstrated that the disentangled features produced by total disentanglement apply to a variety of tasks, including font recognition, character recognition, and one-shot font image generation.
Image Cropping under Design Constraints
We explore two derived approaches, a proposal-based approach, and a heatmap-based approach, and we construct a dataset for evaluating the performance of the proposed approaches on image cropping under design constraints.
Towards Diverse and Consistent Typography Generation
In this work, we consider the typography generation task that aims at producing diverse typographic styling for the given graphic document.
Self-Supervised Difference Detection for Weakly-Supervised Semantic Segmentation
In this paper, to make the most of such mapping functions, we assume that the results of the mapping function include noise, and we improve the accuracy by removing noise.
Ranked #24 on
Semantic Segmentation
on PASCAL VOC 2012 val
An Integration of Bottom-up and Top-Down Salient Cues on RGB-D Data: Saliency from Objectness vs. Non-Objectness
Bottom-up and top-down visual cues are two types of information that helps the visual saliency models.
Saliency Detection by Forward and Backward Cues in Deep-CNNs
As prior knowledge of objects or object features helps us make relations for similar objects on attentional tasks, pre-trained deep convolutional neural networks (CNNs) can be used to detect salient objects on images regardless of the object class is in the network knowledge or not.
