Despite recent text-to-image models achieving high-fidelity text rendering, they still struggle with long or multiple texts due to diluted global attention. We propose DC-Text, a training-free visual text generation method that adopts a divide-and-conquer strategy, leveraging the reliable short-text generation of Multi-Modal Diffusion Transformers. Our method first decomposes a prompt by extracting and dividing the target text, then assigns each to a designated region. To accurately render each segment within their regions while preserving overall image coherence, we introduce two attention masks—Text-Focus and Context-Expansion—applied sequentially during denoising. Additionally, Localized Noise Initialization further improves text accuracy and region alignment without increasing computational cost. Extensive experiments on single- and multi-sentence benchmarks show that DCText achieves the best text accuracy without compromising image quality while also delivering the lowest generation latency.

Developing effective visual inspection models remains challenging due to the scarcity of defect data. While image generation models have been used to synthesize defect images, producing highly realistic defects remains difficult. We propose DefectFill, a novel method for realistic defect generation that requires only a few reference defect images. It leverages a fine-tuned inpainting diffusion model, optimized with our custom loss functions incorporating defect, object, and attention terms. It enables precise capture of detailed, localized defect features and their seamless integration into defect-free objects. Additionally, our Low-Fidelity Selection method further enhances the defect sample quality. Experiments show that DefectFill generates high-quality defect images, enabling visual inspection models to achieve state- of-the-art performance on the MVTec AD dataset.

Studies on supervised machine learning (ML) recommend involving workers from various backgrounds in training dataset labeling to reduce algorithmic bias. Moreover, sophisticated tasks for categorizing objects in images are necessary to improve ML performance, further complicating micro-tasks. This study aims to develop a task design incorporating the fair participation of people, regardless of their specific backgrounds or task’s difficulty. By collaborating with 75 labelers from diverse backgrounds for 3 months, we analyzed workers’ log-data and relevant narratives to identify the task’s hurdles and helpers. The findings revealed that workers’ decision-making tendencies were affected by the "community" that positively helps workers. Also, the machine’s feedback perceived by workers could make people easily engaged in works. Based on these findings, we suggest an extended human-in-the-loop approach that connects labelers, machines, and communities rather than isolating individual workers.