VoQA: Visual-only Question Answering

• URL: http://arxiv.org/abs/2505.14227v1
• Date: Tue, 20 May 2025 11:37:49 GMT
• Title: VoQA: Visual-only Question Answering
• Authors: Luyang Jiang, Jianing An, Jie Luo, Wenjun Wu, Lei Huang,
• Abstract summary: We propose Visual-only Question Answering (VoQA), a novel multimodal task in which questions are visually embedded within images.<n>This requires models to locate, recognize, and reason over visually embedded textual questions.<n>We introduce Guided Response Triggering Supervised Fine-tuning (GRT-SFT), a structured fine-tuning strategy that guides the model to perform step-by-step reasoning purely based on visual input.
• Score: 7.251596370310251
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose Visual-only Question Answering (VoQA), a novel multimodal task in which questions are visually embedded within images, without any accompanying textual input. This requires models to locate, recognize, and reason over visually embedded textual questions, posing challenges for existing large vision-language models (LVLMs), which show notable performance drops even with carefully designed prompts. To bridge this gap, we introduce Guided Response Triggering Supervised Fine-tuning (GRT-SFT), a structured fine-tuning strategy that guides the model to perform step-by-step reasoning purely based on visual input, significantly improving model performance. Our work enhances models' capacity for human-like visual understanding in complex multimodal scenarios, where information, including language, is perceived visually.

Related papers

• Understand, Think, and Answer: Advancing Visual Reasoning with Large Multimodal Models [26.14137626882127]
  Large Multimodal Models (LMMs) have recently demonstrated remarkable visual understanding performance on both vision-language and vision-centric tasks.<n>We present a unified visual reasoning mechanism that enables LMMs to solve complicated compositional problems.<n>Our trained model, Griffon-R, has the ability of end-to-end automatic understanding, self-thinking, and reasoning answers.
  arXiv  Detail & Related papers  (2025-05-27T05:50:25Z)
• Instruction Tuning-free Visual Token Complement for Multimodal LLMs [51.138806401996696]
  multimodal large language models (MLLMs) have promised an elegant bridge between vision and language. We propose a Visual Token Complement framework (VTC) that helps MLLMs regain the missing visual features. Our VTC integrates text-to-image generation as a guide to identifying the text-irrelevant features, and a visual selector is then developed to generate complementary visual tokens.
  arXiv  Detail & Related papers  (2024-08-09T12:13:01Z)
• InsightSee: Advancing Multi-agent Vision-Language Models for Enhanced Visual Understanding [12.082379948480257]
  This paper proposes InsightSee, a multi-agent framework to enhance vision-language models' capabilities in handling complex visual understanding scenarios. The framework comprises a description agent, two reasoning agents, and a decision agent, which are integrated to refine the process of visual information interpretation. The proposed framework outperforms state-of-the-art algorithms in 6 out of 9 benchmark tests, with a substantial advancement in multimodal understanding.
  arXiv  Detail & Related papers  (2024-05-31T13:56:55Z)
• Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models [81.71651422951074]
  Chain-of-Spot (CoS) method is a novel approach that enhances feature extraction by focusing on key regions of interest. This technique allows LVLMs to access more detailed visual information without altering the original image resolution. Our empirical findings demonstrate a significant improvement in LVLMs' ability to understand and reason about visual content.
  arXiv  Detail & Related papers  (2024-03-19T17:59:52Z)
• SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant [48.220285886328746]
  We introduce a novel framework named SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant. SQ-LLaVA exhibits proficiency in generating flexible and meaningful image-related questions while analyzing the visual clue and prior language knowledge. Fine-tuning SQ-LLaVA on higher-quality instruction data shows a performance improvement compared with traditional visual-instruction tuning methods.
  arXiv  Detail & Related papers  (2024-03-17T18:42:38Z)
• Question Aware Vision Transformer for Multimodal Reasoning [14.188369270753347]
  We introduce QA-ViT, a Question Aware Vision Transformer approach for multimodal reasoning. It embeds question awareness directly within the vision encoder. This integration results in dynamic visual features focusing on relevant image aspects to the posed question.
  arXiv  Detail & Related papers  (2024-02-08T08:03:39Z)
• Improving In-Context Learning in Diffusion Models with Visual Context-Modulated Prompts [83.03471704115786]
  We introduce improved Prompt Diffusion (iPromptDiff) in this study. iPromptDiff integrates an end-to-end trained vision encoder that converts visual context into an embedding vector. We show that a diffusion-based vision foundation model, when equipped with this visual context-modulated text guidance and a standard ControlNet structure, exhibits versatility and robustness across a variety of training tasks.
  arXiv  Detail & Related papers  (2023-12-03T14:15:52Z)
• Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding [55.65727739645824]
  Chat-UniVi is a Unified Vision-language model capable of comprehending and engaging in conversations involving images and videos. We employ a set of dynamic visual tokens to uniformly represent images and videos. We leverage a multi-scale representation, enabling the model to perceive both high-level semantic concepts and low-level visual details.
  arXiv  Detail & Related papers  (2023-11-14T10:11:36Z)
• Unifying Image Processing as Visual Prompting Question Answering [62.84955983910612]
  Image processing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent vision applications. Traditionally, task-specific models are developed for individual tasks and designing such models requires distinct expertise. We propose a universal model for general image processing that covers image restoration, image enhancement, image feature extraction tasks.
  arXiv  Detail & Related papers  (2023-10-16T15:32:57Z)
• Look, Remember and Reason: Grounded reasoning in videos with language models [5.3445140425713245]
  Multi-temporal language models (LM) have recently shown promising performance in high-level reasoning tasks on videos. We propose training an LM end-to-end on low-level surrogate tasks, including object detection, re-identification, tracking, to endow the model with the required low-level visual capabilities. We demonstrate the effectiveness of our framework on diverse visual reasoning tasks from the ACRE, CATER, Something-Else and STAR datasets.
  arXiv  Detail & Related papers  (2023-06-30T16:31:14Z)
• mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections [104.14624185375897]
  mPLUG is a new vision-language foundation model for both cross-modal understanding and generation. It achieves state-of-the-art results on a wide range of vision-language downstream tasks, such as image captioning, image-text retrieval, visual grounding and visual question answering.
  arXiv  Detail & Related papers  (2022-05-24T11:52:06Z)

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.