Spoken question answering for visual queries

• URL: http://arxiv.org/abs/2505.23308v1
• Date: Thu, 29 May 2025 10:06:48 GMT
• Title: Spoken question answering for visual queries
• Authors: Nimrod Shabtay, Zvi Kons, Avihu Dekel, Hagai Aronowitz, Ron Hoory, Assaf Arbelle,
• Abstract summary: This work aims to create a system that enables user interaction through both speech and images.<n>The resulting multi-modal model has textual, visual, and spoken inputs and can answer spoken questions on images.
• Score: 14.834200714168546
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Question answering (QA) systems are designed to answer natural language questions. Visual QA (VQA) and Spoken QA (SQA) systems extend the textual QA system to accept visual and spoken input respectively. This work aims to create a system that enables user interaction through both speech and images. That is achieved through the fusion of text, speech, and image modalities to tackle the task of spoken VQA (SVQA). The resulting multi-modal model has textual, visual, and spoken inputs and can answer spoken questions on images. Training and evaluating SVQA models requires a dataset for all three modalities, but no such dataset currently exists. We address this problem by synthesizing VQA datasets using two zero-shot TTS models. Our initial findings indicate that a model trained only with synthesized speech nearly reaches the performance of the upper-bounding model trained on textual QAs. In addition, we show that the choice of the TTS model has a minor impact on accuracy.

Related papers

• Generalizing Visual Question Answering from Synthetic to Human-Written Questions via a Chain of QA with a Large Language Model [4.41132900194195]
  We propose a new method called it chain of QA for human-written questions (CoQAH) CoQAH utilizes a sequence of QA interactions between a large language model and a VQA model trained on synthetic data to reason and derive logical answers for human-written questions. We tested the effectiveness of CoQAH on two types of human-written VQA datasets for 3D-rendered and chest X-ray images.
  arXiv  Detail & Related papers  (2024-01-12T06:49:49Z)
• Zero-shot Translation of Attention Patterns in VQA Models to Natural Language [65.94419474119162]
  ZS-A2T is a framework that translates the transformer attention of a given model into natural language without requiring any training. We consider this in the context of Visual Question Answering (VQA) Our framework does not require any training and allows the drop-in replacement of different guiding sources.
  arXiv  Detail & Related papers  (2023-11-08T22:18:53Z)
• Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts [54.072432123447854]
  Visual question answering (VQA) is the task of answering questions about an image. Answering the question requires commonsense knowledge, world knowledge, and reasoning about ideas and concepts not present in the image. We propose a framework that uses language guidance (LG) in the form of rationales, image captions, scene graphs, etc to answer questions more accurately.
  arXiv  Detail & Related papers  (2023-10-31T03:54:11Z)
• Making the V in Text-VQA Matter [1.2962828085662563]
  Text-based VQA aims at answering questions by reading the text present in the images. Recent studies have shown that the question-answer pairs in the dataset are more focused on the text present in the image. The models trained on this dataset predict biased answers due to the lack of understanding of visual context.
  arXiv  Detail & Related papers  (2023-08-01T05:28:13Z)
• NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario [77.14723238359318]
  NuScenesQA is the first benchmark for VQA in the autonomous driving scenario, encompassing 34K visual scenes and 460K question-answer pairs. We leverage existing 3D detection annotations to generate scene graphs and design question templates manually. We develop a series of baselines that employ advanced 3D detection and VQA techniques.
  arXiv  Detail & Related papers  (2023-05-24T07:40:50Z)
• Zero-Shot Video Question Answering via Frozen Bidirectional Language Models [89.71617065426146]
  Video question answering (VideoQA) is a complex task that requires diverse multi-modal data for training. Recent methods consider zero-shot settings with no manual annotation of visual question-answer. We build on frozen autoregressive language models (BiLM) and show that such an approach provides a stronger and cheaper alternative for zero-shot VideoQA.
  arXiv  Detail & Related papers  (2022-06-16T13:18:20Z)
• From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data [27.160303686163164]
  Visual question answering (VQA) for remote sensing scene has great potential in intelligent human-computer interaction system. No object annotations are available in RSVQA datasets, which makes it difficult for models to exploit informative region representation. There are questions with clearly different difficulty levels for each image in the RSVQA task. A multi-level visual feature learning method is proposed to jointly extract language-guided holistic and regional image features.
  arXiv  Detail & Related papers  (2022-05-06T11:37:00Z)
• DUAL: Textless Spoken Question Answering with Speech Discrete Unit Adaptive Learning [66.71308154398176]
  Spoken Question Answering (SQA) has gained research attention and made remarkable progress in recent years. Existing SQA methods rely on Automatic Speech Recognition (ASR) transcripts, which are time and cost-prohibitive to collect. This work proposes an ASR transcript-free SQA framework named Discrete Unit Adaptive Learning (DUAL), which leverages unlabeled data for pre-training and is fine-tuned by the SQA downstream task.
  arXiv  Detail & Related papers  (2022-03-09T17:46:22Z)
• Image Captioning for Effective Use of Language Models in Knowledge-Based Visual Question Answering [17.51860125438028]
  We propose to use a unimodal (text-only) train and inference procedure based on automatic off-the-shelf captioning of images and pretrained language models. Our results on a visual question answering task which requires external knowledge (OK-VQA) show that our text-only model outperforms pretrained multimodal (image-text) models of comparable number of parameters.
  arXiv  Detail & Related papers  (2021-09-15T14:11:29Z)
• Look Before you Speak: Visually Contextualized Utterances [88.58909442073858]
  We create a task for predicting utterances in a video using both visual frames and transcribed speech as context. By exploiting the large number of instructional videos online, we train a model to solve this task at scale, without the need for manual annotations. Our model achieves state-of-the-art performance on a number of downstream VideoQA benchmarks.
  arXiv  Detail & Related papers  (2020-12-10T14:47:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.