Professionals working in technical domain typically hand-draw (on whiteboard, paper, etc.) technical diagrams (e.g., flowcharts, block diagrams, etc.) during discussions; however, if they want to edit these later, it needs to be drawn from scratch. Modern day VLMs have made tremendous progress in image understanding but they struggle when it comes to understanding technical diagrams. One way to overcome this problem is to fine-tune on real world hand-drawn images, but it is not practically possible to generate large number of such images. In this paper, we introduce a large synthetically generated corpus (reflective of real world images) for training VLMs and subsequently evaluate VLMs on a smaller corpus of hand-drawn images (with the help of humans). We introduce several new self-supervision tasks for training and perform extensive experiments with various baseline models and fine-tune Llama 3.2 11B-instruct model on synthetic images on these tasks to obtain LLama-VL-TUG, which significantly improves the ROUGE-L performance of Llama 3.2 11B-instruct by 2.14x and achieves the best all-round performance across all baseline models. On real-world images, human evaluation reveals that we achieve minimum compilation errors across all baselines in 7 out of 8 diagram types and improve the average F1 score of Llama 3.2 11B-instruct by 6.97x.

This paper presents our approach to SemEval-2025 Task 11 (Track A): Bridging the Gap in Text-Based Emotion Detection, with a focus on multi-label emotion classification for the English dataset. Our methodology leverages an ensemble of transformer-based models, incorporating full fine-tuning along with additional classification layers to enhance predictive performance. Through extensive experimentation, we demonstrate that fine-tuning significantly improves emotion classification accuracy compared to baseline models. Furthermore, we provide an in-depth analysis of the dataset, highlighting key patterns and challenges. The study also evaluates the impact of ensemble modeling on performance, demonstrating its effectiveness in capturing nuanced emotional expressions. Finally, we outline potential directions for further refinement and domain-specific adaptations to enhance model robustness.