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Found 10 papers, 1 papers with code
DesignProbe: A Graphic Design Benchmark for Multimodal Large Language Models
This complexity makes the comprehension of graphic design challenging, for it needs the capability to both recognize the design elements and understand the design.
Desigen: A Pipeline for Controllable Design Template Generation
In this paper, we present Desigen, an automatic template creation pipeline which generates background images as well as harmonious layout elements over the background.
Spot the Error: Non-autoregressive Graphic Layout Generation with Wireframe Locator
Furthermore, based on our observation that pixel space is more sensitive in capturing spatial patterns of graphic layouts (e. g., overlap, alignment), we propose a learning-based locator to detect erroneous tokens which takes the wireframe image rendered from the generated layout sequence as input.
Towards General and Efficient Online Tuning for Spark
The distributed data analytic system -- Spark is a common choice for processing massive volumes of heterogeneous data, while it is challenging to tune its parameters to achieve high performance.
CANVASEMB: Learning Layout Representation with Large-scale Pre-training for Graphic Design
Layout representation, which models visual elements in a canvas and their inter-relations, plays a crucial role in graphic design intelligence.
Towards Generating Math Word Problems from Equations and Topics
A math word problem is a narrative with a specific topic that provides clues to the correct equation with numerical quantities and variables therein.
Neural Math Word Problem Solver with Reinforcement Learning
Experimental results show that (1) The copy and alignment mechanism is effective to address the two issues; (2) Reinforcement learning leads to better performance than maximum likelihood on this task; (3) Our neural model is complementary to the feature-based model and their combination significantly outperforms the state-of-the-art results.
Using Intermediate Representations to Solve Math Word Problems
To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system.
Learning Fine-Grained Expressions to Solve Math Word Problems
This method learns the mappings between math concept phrases in math word problems and their math expressions from training data.
