Aquila-plus: Prompt-Driven Visual-Language Models for Pixel-Level Remote Sensing Image Understanding

• URL: http://arxiv.org/abs/2411.06142v1
• Date: Sat, 09 Nov 2024 10:42:57 GMT
• Title: Aquila-plus: Prompt-Driven Visual-Language Models for Pixel-Level Remote Sensing Image Understanding
• Authors: Kaixuan Lu,
• Abstract summary: We propose a mask-text instruction tuning method called Aquila-plus to achieve pixel-level visual understanding. Aquila-plus uses a convolutional CLIP as the visual encoder and employs a mask-aware visual extractor to extract precise visual mask features. Experimental results demonstrate that Aquila-plus outperforms existing methods in various region understanding tasks.
• Score: 0.0
• License:
• Abstract: The recent development of vision language models (VLMs) has led to significant advances in visual-language integration through visual instruction tuning, and they have rapidly evolved in the field of remote sensing image understanding, demonstrating their powerful capabilities. However, existing RSVLMs mainly focus on image-level or frame-level understanding, making it difficult to achieve fine-grained pixel-level visual-language alignment. Additionally, the lack of mask-based instructional data limits their further development. In this paper, we propose a mask-text instruction tuning method called Aquila-plus, which extends the capabilities of RSVLMs to achieve pixel-level visual understanding by incorporating fine-grained mask regions into language instructions. To achieve this, we first meticulously constructed a mask region-text dataset containing 100K samples, and then designed a visual-language model by injecting pixel-level representations into a large language model (LLM). Specifically, Aquila-plus uses a convolutional CLIP as the visual encoder and employs a mask-aware visual extractor to extract precise visual mask features from high-resolution inputs. Experimental results demonstrate that Aquila-plus outperforms existing methods in various region understanding tasks, showcasing its novel capabilities in pixel-level instruction tuning.

Related papers

• Aquila: A Hierarchically Aligned Visual-Language Model for Enhanced Remote Sensing Image Comprehension [6.29665399879184]
  We present Aquila, an advanced visual language foundation model for remote sensing images. Aquila enables richer visual feature representation and more precise visual-language feature alignment. We validate the effectiveness of Aquila through extensive quantitative experiments and qualitative analyses.
  arXiv  Detail & Related papers  (2024-11-09T05:31:56Z)
• ForgeryGPT: Multimodal Large Language Model For Explainable Image Forgery Detection and Localization [49.992614129625274]
  ForgeryGPT is a novel framework that advances the Image Forgery Detection and localization task. It captures high-order correlations of forged images from diverse linguistic feature spaces. It enables explainable generation and interactive dialogue through a newly customized Large Language Model (LLM) architecture.
  arXiv  Detail & Related papers  (2024-10-14T07:56:51Z)
• 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)
• OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding [112.87441334765693]
  OMG-LLaVA is a new framework combining powerful pixel-level vision understanding with reasoning abilities. It can accept various visual and text prompts for flexible user interaction. OMG-LLaVA achieves image-level, object-level, and pixel-level reasoning and understanding in a single model.
  arXiv  Detail & Related papers  (2024-06-27T17:59:01Z)
• Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want [58.091825321168514]
  We introduce the Draw-and-Understand project: a new model, a multi-domain dataset, and a challenging benchmark for visual prompting. Specifically, we propose a new end-to-end trained Multimodal Large Language Model (MLLM) that connects a vision encoder, a visual prompt encoder and an LLM. To advance visual prompting research for MLLMs, we introduce MDVP-Data and MDVP-Bench.
  arXiv  Detail & Related papers  (2024-03-29T16:26:20Z)
• Multi-modal Instruction Tuned LLMs with Fine-grained Visual Perception [63.03288425612792]
  We propose bfAnyRef, a general MLLM model that can generate pixel-wise object perceptions and natural language descriptions from multi-modality references. Our model achieves state-of-the-art results across multiple benchmarks, including diverse modality referring segmentation and region-level referring expression generation.
  arXiv  Detail & Related papers  (2024-03-05T13:45:46Z)
• Osprey: Pixel Understanding with Visual Instruction Tuning [15.094943732551018]
  Osprey is a mask-text instruction tuning approach to extend MLLMs by incorporating fine-grained mask regions into language instruction. To achieve this goal, we first curate a mask-based region-text dataset with 724K samples, and then design a vision-language model by injecting pixel-level representation into LLM. Specifically, Osprey adopts a convolutional CLIP backbone as the vision encoder and employs a mask-aware visual extractor to extract precise visual mask features from high resolution input.
  arXiv  Detail & Related papers  (2023-12-15T18:58:11Z)
• Position-Enhanced Visual Instruction Tuning for Multimodal Large Language Models [50.07056960586183]
  We propose Position-enhanced Visual Instruction Tuning (PVIT) to extend the functionality of Multimodal Large Language Models (MLLMs) This integration promotes a more detailed comprehension of images for the MLLM. We present both quantitative experiments and qualitative analysis that demonstrate the superiority of the proposed model.
  arXiv  Detail & Related papers  (2023-08-25T15:33:47Z)
• VLMAE: Vision-Language Masked Autoencoder [21.97700040013084]
  We propose a vision-language masked autoencoder framework (VLMAE) for vision-language pre-training. VLMAE employs visual generative learning, facilitating the model to acquire fine-grained and unbiased features.
  arXiv  Detail & Related papers  (2022-08-19T14:39:18Z)
• Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers [46.275416873403614]
  We propose Pixel-BERT to align image pixels with text by deep multi-modal transformers that jointly learn visual and language embedding. Our approach makes the most state-of-the-arts in downstream tasks, including Visual Question Answering (VQA), image-text retrieval, Natural Language for Visual Reasoning for Real (NLVR)
  arXiv  Detail & Related papers  (2020-04-02T07:39:28Z)

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.