SkillCLIP: Skill Aware Modality Fusion Visual Question Answering (Student Abstract)

Atharva Naik, Yash Parag Butala, Navaneethan Vaikunthan, Raghav Kapoor

Abstract: When humans are posed with a difficult problem, they often approach it by identifying key skills, honing them, and finally effectively combining them. We propose a novel method and apply it for the VizWiz VQA task to predict the visual skills needed to answer a question, and leverage expert modules to produce intermediary outputs and fuse them in a skill-aware manner. Unlike prior works in visual question-answering (VQA) that use intermediate outputs such as detected objects and Optical Character Recognition (OCR), our approach explicitly guides the model with a skill embedding on what to focus on. While our results show that using skill-aware fusion outperforms skill-unaware models for only a subset of questions, we believe our results provide interesting directions for future work. We also release our code, model, and illustrative demonstrations for future research purposes.