Modularized Zero-shot VQA with Pre-trained Models
This addresses the challenge of multi-step reasoning in VQA for AI systems, offering a more interpretable approach, though it is incremental as it builds on existing pre-trained models.
The paper tackles the problem of zero-shot visual question answering (VQA) by proposing a modularized network that decomposes questions into sub-reasoning steps, leveraging pre-trained models without adaptation, and demonstrates effectiveness on two VQA benchmarks with improved interpretability.
Large-scale pre-trained models (PTMs) show great zero-shot capabilities. In this paper, we study how to leverage them for zero-shot visual question answering (VQA). Our approach is motivated by a few observations. First, VQA questions often require multiple steps of reasoning, which is still a capability that most PTMs lack. Second, different steps in VQA reasoning chains require different skills such as object detection and relational reasoning, but a single PTM may not possess all these skills. Third, recent work on zero-shot VQA does not explicitly consider multi-step reasoning chains, which makes them less interpretable compared with a decomposition-based approach. We propose a modularized zero-shot network that explicitly decomposes questions into sub reasoning steps and is highly interpretable. We convert sub reasoning tasks to acceptable objectives of PTMs and assign tasks to proper PTMs without any adaptation. Our experiments on two VQA benchmarks under the zero-shot setting demonstrate the effectiveness of our method and better interpretability compared with several baselines.