Precision Empowers, Excess Distracts: Visual Question Answering With Dynamically Infused Knowledge In Language Models
Authors: Manas Jhalani, Annervaz K M, Pushpak Bhattacharyya


Reviewer paper summaries: This paper introduces a novel approach in knowledge-based visual question answering, leveraging a dynamic triple extraction method to enhance questions with relevant external knowledge derived from knowledge graphs.
Summary of strengths: - This paper presents a dynamic triple filtering method that provides question-specific triples rather than a fixed number, serving as context to answer the given question.
- A comprehensive benchmark across all three datasets would facilitate a thorough evaluation of the VQA model's strengths and weaknesses under diverse settings.
- An augmented knowledge base tailored for the CRIC VQA dataset.
Summary of weaknesses: - While the comparison across the three datasets in the study is valuable, it would be interesting to see how the proposed method performs on open-domain datasets like OK-VQA and A-OKVQA. These datasets are designed to test visual question answering models' ability to leverage external knowledge and reasoning, which could provide additional insights into the strengths and limitations of the proposed approach.

- The paper's rationale for selecting OFA over alternative methods within the prediction module remains unexplained. Further clarification on the authors' decision-making process regarding this methodological choice is warranted.
Comments, suggestions and typos: - To enhance the robustness and generalizability of the proposed methods, a comparative analysis with an open-domain dataset is recommended. This would allow for a more comprehensive evaluation of the methods' performance across a wider range of tasks and scenarios, ultimately strengthening the validity and applicability of the research findings.
- While the authors have conducted experiments using various methods in this field, a more thorough explanation of the rationale and strategy behind their methodological choices is warranted.
Overall assessment scores: 4