Layout generation is a critical step in graphic design to achieve meaningful compositions of elements. Most previous works view it as a sequence generation problem by concatenating element attribute tokens (i.e., category, size, position). So far the autoregressive approach (AR) has achieved promising results, but is still limited in global context modeling and suffers from error propagation since it can only attend to the previously generated tokens. Recent non-autoregressive attempts (NAR) have shown competitive results, which provides a wider context range and the flexibility to refine with iterative decoding. However, current works only use simple heuristics to recognize erroneous tokens for refinement which is inaccurate. This paper first conducts an in-depth analysis to better understand the difference between the AR and NAR framework. 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. We show that it serves as a complementary modality to the element sequence in object space and contributes greatly to the overall performance. Experiments on two public datasets show that our approach outperforms both AR and NAR baselines. Extensive studies further prove the effectiveness of different modules with interesting findings. Our code will be available at https://github.com/ffffatgoose/SpotError.

Few-shot sequence labeling is a general problem formulation for many natural language understanding tasks in data-scarcity scenarios, which require models to generalize to new types via only a few labeled examples. Recent advances mostly adopt metric-based meta-learning and thus face the challenges of modeling the miscellaneousOtherprototype and the inability to generalize to classes with large domain gaps. To overcome these challenges, we propose a decomposed meta-learning framework for few-shot sequence labeling that breaks down the task into few-shot mention detection and few-shot type classification, and sequentially tackles them via meta-learning. Specifically, we employ model-agnostic meta-learning (MAML) to prompt the mention detection model to learn boundary knowledge shared across types. With the detected mention spans, we further leverage the MAML-enhanced span-level prototypical network for few-shot type classification. In this way, the decomposition framework bypasses the requirement of modeling the miscellaneousOtherprototype. Meanwhile, the adoption of the MAML algorithm enables us to explore the knowledge contained in support examples more efficiently, so that our model can quickly adapt to new types using only a few labeled examples. Under our framework, we explore a basic implementation that uses two separate models for the two subtasks. We further propose a joint model to reduce model size and inference time, making our framework more applicable for scenarios with limited resources. Extensive experiments on nine benchmark datasets, including named entity recognition, slot tagging, event detection, and part-of-speech tagging, show that the proposed approach achieves start-of-the-art performance across various few-shot sequence labeling tasks.

It is a common practice for designers to create digital prototypes from a mock-up/screenshot. Reverse engineering graphic design by detecting its components (e.g., text, icon, button) helps expedite this process. This paper first conducts a statistical analysis to emphasize the importance of relations in graphic layouts, which further motivates us to incorporate relation modeling into component detection. Built on the current state-of-the-art DETR (DEtection TRansformer), we introduce a learnable relation matrix to model class correlations. Specifically, the matrix will be added in the DETR decoder to update the query-to-query self-attention. Experiment results on three public datasets show that our approach achieves better performance than several strong baselines. We further visualize the learnt relation matrix and observe some reasonable patterns. Moreover, we show an application of component detection where we leverage the detection outputs as augmented training data for layout generation, which achieves promising results.