VGA: Vision GUI Assistant -- Minimizing Hallucinations through Image-Centric Fine-Tuning

• URL: http://arxiv.org/abs/2406.14056v3
• Date: Mon, 04 Nov 2024 09:31:06 GMT
• Title: VGA: Vision GUI Assistant -- Minimizing Hallucinations through Image-Centric Fine-Tuning
• Authors: Ziyang Meng, Yu Dai, Zezheng Gong, Shaoxiong Guo, Minglong Tang, Tongquan Wei,
• Abstract summary: We introduce VGA, a fine-tuned model designed for comprehensive Graphical User Interface (GUI) understanding. Our model aims to enhance the interpretation of visual data of GUI and reduce hallucinations. Our dataset and fine-tuning script will be released soon.
• Score: 6.035805925050573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in Large Vision-Language Models (LVLMs) have significantly improve performance in image comprehension tasks, such as formatted charts and rich-content images. Yet, Graphical User Interface (GUI) pose a greater challenge due to their structured format and detailed textual information. Existing LVLMs often overly depend on internal knowledge and neglect image content, resulting in hallucinations and incorrect responses in GUI comprehension. To address these issues, we introduce VGA, a fine-tuned model designed for comprehensive GUI understanding. Our model aims to enhance the interpretation of visual data of GUI and reduce hallucinations. We first construct a Vision Question Answering (VQA) dataset of 63.8k high-quality examples with our propose Referent Method, which ensures the model's responses are highly depend on visual content within the image. We then design a two-stage fine-tuning method called Foundation and Advanced Comprehension (FAC) to enhance both the model's ability to extract information from image content and alignment with human intent. Experiments show that our approach enhances the model's ability to extract information from images and achieves state-of-the-art results in GUI understanding tasks. Our dataset and fine-tuning script will be released soon.

Related papers

• VoQA: Visual-only Question Answering [7.251596370310251]
  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.
  arXiv  Detail & Related papers  (2025-05-20T11:37:49Z)
• On the Limitations of Vision-Language Models in Understanding Image Transforms [0.9217021281095907]
  Vision Language Models (VLMs) have demonstrated significant potential in various downstream tasks. This paper investigates the image-level understanding of VLMs, specifically CLIP by OpenAI and SigLIP by Google. Our findings reveal that these models lack comprehension of multiple image-level augmentations.
  arXiv  Detail & Related papers  (2025-03-12T20:58:16Z)
• Do we Really Need Visual Instructions? Towards Visual Instruction-Free Fine-tuning for Large Vision-Language Models [127.38740043393527]
  We propose ViFT, a visual instruction-free fine-tuning framework for LVLMs. We only require the text-only instructions and image caption data during training, to separately learn the task-solving and visual perception abilities. Experimental results demonstrate that ViFT can achieve state-of-the-art performance on several visual reasoning and visual instruction following benchmarks.
  arXiv  Detail & Related papers  (2025-02-17T04:38:12Z)
• Context Canvas: Enhancing Text-to-Image Diffusion Models with Knowledge Graph-Based RAG [6.701537544179892]
  We introduce a novel approach to enhance the capabilities of text-to-image models by incorporating a graph-based RAG. Our system dynamically retrieves detailed character information and relational data from the knowledge graph, enabling the generation of visually accurate and contextually rich images.
  arXiv  Detail & Related papers  (2024-12-12T18:59:41Z)
• ShowUI: One Vision-Language-Action Model for GUI Visual Agent [80.50062396585004]
  Building Graphical User Interface (GUI) assistants holds significant promise for enhancing human workflow productivity. We develop a vision-language-action model in digital world, namely ShowUI, which features the following innovations. ShowUI, a lightweight 2B model using 256K data, achieves a strong 75.1% accuracy in zero-shot screenshot grounding.
  arXiv  Detail & Related papers  (2024-11-26T14:29:47Z)
• Enhancing Vision Models for Text-Heavy Content Understanding and Interaction [0.0]
  We build a visual chat application integrating CLIP for image encoding and a model from the Massive Text Embedding Benchmark. The aim of the project is to increase and also enhance the advance vision models' capabilities in understanding complex visual textual data interconnected data.
  arXiv  Detail & Related papers  (2024-05-31T15:17:47Z)
• Enhancing Large Vision Language Models with Self-Training on Image Comprehension [131.14381425260706]
  We introduce Self-Training on Image (STIC), which emphasizes a self-training approach specifically for image comprehension. First, the model self-constructs a preference for image descriptions using unlabeled images. To further self-improve reasoning on the extracted visual information, we let the model reuse a small portion of existing instruction-tuning data.
  arXiv  Detail & Related papers  (2024-05-30T05:53:49Z)
• Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want [58.091825321168514]
  We present the Draw-and-Understand framework, exploring how to integrate visual prompting understanding capabilities into Multimodal Large Language Models (MLLMs) Visual prompts allow users to interact through multi-modal instructions, enhancing the models' interactivity and fine-grained image comprehension. In this framework, we propose a general architecture adaptable to different pre-trained MLLMs, enabling it to recognize various types of visual prompts.
  arXiv  Detail & Related papers  (2024-03-29T16:26:20Z)
• 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)
• GPT4Image: Can Large Pre-trained Models Help Vision Models on Perception Tasks? [51.22096780511165]
  We present a new learning paradigm in which the knowledge extracted from large pre-trained models are utilized to help models like CNN and ViT learn enhanced representations. We feed detailed descriptions into a pre-trained encoder to extract text embeddings with rich semantic information that encodes the content of images.
  arXiv  Detail & Related papers  (2023-06-01T14:02:45Z)
• Understanding ME? Multimodal Evaluation for Fine-grained Visual Commonsense [98.70218717851665]
  It is unclear whether the models really understand the visual scene and underlying commonsense knowledge due to limited evaluation data resources. We present a Multimodal Evaluation (ME) pipeline to automatically generate question-answer pairs to test models' understanding of the visual scene, text, and related knowledge. We then take a step further to show that training with the ME data boosts the model's performance in standard VCR evaluation.
  arXiv  Detail & Related papers  (2022-11-10T21:44:33Z)

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.