| Challenge: | Large multi-modal models (LMMs) are capable of visual question answering (VQA) with unprecedented accuracy. |
| Approach: | They propose a calibration score that can be used to quantify uncertainty in visual question answering models. |
| Outcome: | The proposed calibration score is better calibrated than in text-only models for in-context learning. |
Similar Papers
Uncovering the Full Potential of Visual Grounding Methods in VQA (2024.acl-long)
Copied to clipboard
| Challenge: | Visual Grounding (VG) methods in VQA aim to strengthen a model's reliance on question-relevant visual information. |
| Approach: | They propose to strengthen a model's reliance on question-relevant visual information by using a visual grounding method that is based on a question-related visual input. |
| Outcome: | The proposed methods can be much more effective when evaluation conditions are corrected. |
Teaching Vision-Language Models to Ask: Resolving Ambiguity in Visual Questions (2025.acl-long)
Copied to clipboard
| Challenge: | Existing research addresses ambiguous visual questions by rephrasing questions, but it fails to address the inherently interactive nature of user interactions with visual language models (VLMs). Existing studies focus on re-phrase questions, and lack of a benchmark to assess VLMs’ capacity for resolving ambiguities through interaction. |
| Approach: | They propose a visual question answering task that provides a natural language answer to a question based on a given image and an automated pipeline to generate ambiguity-clarification question pairs. |
| Outcome: | The proposed benchmark targets three common categories of ambiguity in visual question answering (VQA) context and encompasses various VQA scenarios. |
Beyond Accuracy: A Consolidated Tool for Visual Question Answering Benchmarking (2021.emnlp-demo)
Copied to clipboard
| Challenge: | Existing evaluation tools for general Visual Question Answering (VQA) systems are limited to answering accuracy, but they can be used to evaluate performance in real-world scenarios. |
| Approach: | They propose a browser-based benchmarking tool with an API for easy integration of new models and datasets to keep up with the fast-changing landscape of VQA. |
| Outcome: | The proposed tool tests generalization capabilities of models across multiple datasets and includes metrics that measure biases and uncertainty to further explain model behavior. |
Addressing Blind Guessing: Calibration of Selection Bias in Multiple-Choice Question Answering by Video Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing MCQA benchmarks fail to capture the full reasoning capabilities of video language models due to selection bias. |
| Approach: | They propose a method to reduce selection bias in video-to-text LLMs by suppressing "blind guessing" they propose 'bold' calibration technique to balance selection bias. |
| Outcome: | The proposed method reduces selection bias and improves model performance compared to existing methods. |
MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing Large Vision-Language Models (LVLMs) lack integrated commonsense knowledge . lack of integrated common knowledge limits their robustness and accuracy in VQA . |
| Approach: | They propose a framework to enhance multimodal inference by integrating commonsense reasoning. |
| Outcome: | MAGIC-VQA improves comprehensive benchmark datasets, surpassing existing models in tasks requiring advanced commonsense reasoning. |
What’s Different between Visual Question Answering for Machine “Understanding” Versus for Accessibility? (2022.aacl-main)
Copied to clipboard
| Challenge: | Existing benchmarking datasets for visual question answering focus on machine "understanding" but it remains unclear how progress on those datasets corresponds to improvements in this real-world use case. |
| Approach: | They evaluate the visual question answering task by evaluating a variety of VQA models. |
| Outcome: | The proposed model can achieve high scores on tasks thought to require human-like comprehension, including image tagging and captioning. |
Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to visual question answering (VQA) are not suitable for real-world applications. |
| Approach: | They propose a supervised multi-modal domain adaptation method for visual question answering in images that exploits supervised domain adaptation. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the benchmark VQA 2.0 and VizWiz datasets. |
A Simple Baseline for Knowledge-Based Visual Question Answering (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies emphasize the importance of incorporating both explicit and implicit knowledge to answer questions requiring external knowledge. |
| Approach: | They propose a pipeline that incorporates both explicit and implicit knowledge . their method is training-free and does not require access to external databases or APIs . |
| Outcome: | The proposed method achieves state-of-the-art accuracy on OK-VQA and A-OK-VQ datasets. |
A Multi-modal Debiasing Model with Dynamical Constraint for Robust Visual Question Answering (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have shown that many well-developed Visual Question Answering systems suffer from bias problem. |
| Approach: | They propose a way to mitigate bias problem by subtracting bias score from standard VQA base score. |
| Outcome: | The proposed method improves on the VQA v2.0 and VQA-CP V2,0 datasets. |
CommVQA: Situating Visual Question Answering in Communicative Contexts (2024.emnlp-main)
Copied to clipboard
| Challenge: | Current visual question answering models are trained on image-question pairs in isolation, but the questions people ask are dependent on their informational needs and prior knowledge about the image content. |
| Approach: | They propose a visual question-answer-as-question dataset that contains 1000 images and 8,949 question-announcer pairs to evaluate how situating images within naturalistic contexts shapes visual questions. |
| Outcome: | The proposed dataset contains 1000 images and 8,949 question-answer pairs. |