The field of multimodal understanding and generation is experiencing significant growth, driven by the development of large multimodal models and innovative training methods. Researchers are actively exploring ways to improve the accuracy and interpretability of these models, particularly in tasks such as visual quality assessment and text generation. A key trend is the integration of multiple modalities, such as vision and language, to enable more comprehensive understanding and generation of multimedia content. Additionally, there is a growing focus on creating high-quality datasets and reliable evaluation tools to support the development of next-generation multimodal models. Notable papers in this area include:

• Q-Ponder, which proposes a unified two-stage training framework for visual quality assessment, achieving state-of-the-art performance on quality score regression benchmarks.
• FontAdapter, which enables instant font adaptation in visual text generation, allowing for high-quality and robust font customization across unseen fonts.
• Better Reasoning with Less Data, which introduces a novel quality-driven data selection pipeline for VLM instruction tuning datasets, enhancing the capabilities of VLMs through unified modality scoring.
• A High-Quality Dataset and Reliable Evaluation for Interleaved Image-Text Generation, which presents a large-scale multimodal dataset and an automatic evaluation model for assessing interleaved multimodal outputs.