Fine-Tuning a Large Vision-Language Model for Artwork's Scoring and Critique

Assessing artistic creativity is foundational to creativity research and arts education, yet manual scoring (e.g., Torrance Tests of Creative Thinking) is labor-intensive at scale. Prior machine-learning approaches show promise for visual creativity scoring, but many rely mainly on image features and provide limited or no explanatory feedback. We propose a framework for automated creativity assessment of human paintings by fine-tuning the vision-language model Qwen2-VL-7B with multi-task learning. Our dataset contains 1000 human-created paintings scored on a 1-100 scale and paired with a short human-written description (content or artist explanation). Two expert raters evaluated each work using a five-dimension rubric (originality, color, texture, composition, content) and provided written critiques; we use an 80/20 train-test split. We add a lightweight regression head on the visual encoder output so the model can predict a numerical score and generate rubric-aligned feedback in a single forward pass. By embedding the structured rubric and the artwork description in the system prompt, we constrain the generated text to match the quantitative prediction. Experiments show strong accuracy, achieving Pearson r > 0.97 and MAE about 3.95 on the 100-point scale. Qualitative evaluation indicates the generated feedback is semantically close to expert critiques (average SBERT cosine similarity = 0.798). The proposed approach bridges computer vision and art assessment and offers a scalable tool for creativity research and classroom feedback.

Using a CNN Model to Assess Visual Artwork's Creativity

Assessing artistic creativity has long challenged researchers, with traditional methods proving time-consuming. Recent studies have applied machine learning to evaluate creativity in drawings, but not paintings. Our research addresses this gap by developing a CNN model to automatically assess the creativity of students' paintings. Using a dataset of 600 paintings by professionals and children, our model achieved 90% accuracy and faster evaluation times than human raters. This approach demonstrates the potential of machine learning in advancing artistic creativity assessment, offering a more efficient alternative to traditional methods.

Unsupervised Region-Growing Network for Object Segmentation in Atmospheric Turbulence

In this paper, we present a two-stage unsupervised foreground object segmentation network tailored for dynamic scenes affected by atmospheric turbulence. In the first stage, we utilize averaged optical flow from turbulence-distorted image sequences to feed a novel region-growing algorithm, crafting preliminary masks for each moving object in the video. In the second stage, we employ a U-Net architecture with consistency and grouping losses to further refine these masks optimizing their spatio-temporal alignment. Our approach does not require labeled training data and works across varied turbulence strengths for long-range video. Furthermore, we release the first moving object segmentation dataset of turbulence-affected videos, complete with manually annotated ground truth masks. Our method, evaluated on this new dataset, demonstrates superior segmentation accuracy and robustness as compared to current state-of-the-art unsupervised methods.