Advancing Fine-Grained Visual Understanding with Multi-Scale Alignment in Multi-Modal Models

• URL: http://arxiv.org/abs/2411.09691v1
• Date: Thu, 14 Nov 2024 18:57:07 GMT
• Title: Advancing Fine-Grained Visual Understanding with Multi-Scale Alignment in Multi-Modal Models
• Authors: Wei Wang, Zhaowei Li, Qi Xu, Linfeng Li, YiQing Cai, Botian Jiang, Hang Song, Xingcan Hu, Pengyu Wang, Li Xiao,
• Abstract summary: We introduce a novel fine-grained visual knowledge alignment method. This method integrates multi-scale knowledge of objects, including texts, coordinates, and images. We also present TinyGroundingGPT, a series of compact models optimized for high-level alignments.
• Score: 11.151736352865921
• License:
• Abstract: Multi-modal large language models (MLLMs) have achieved remarkable success in fine-grained visual understanding across a range of tasks. However, they often encounter significant challenges due to inadequate alignment for fine-grained knowledge, which restricts their ability to accurately capture local details and attain a comprehensive global perception. While recent advancements have focused on aligning object expressions with grounding information, they typically lack explicit integration of object images, which contain affluent information beyond mere texts or coordinates. To bridge this gap, we introduce a novel fine-grained visual knowledge alignment method that effectively aligns and integrates multi-scale knowledge of objects, including texts, coordinates, and images. This innovative method is underpinned by our multi-scale fine-grained enhancement data synthesis pipeline, which provides over 300K essential training data to enhance alignment and improve overall performance. Furthermore, we present TinyGroundingGPT, a series of compact models optimized for high-level alignments. With a scale of approximately 3B parameters, TinyGroundingGPT achieves outstanding results in grounding tasks while delivering performance comparable to larger MLLMs in complex visual scenarios.

Related papers

• Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models [50.98559225639266]
  Sub-images with higher semantic relevance to the entire image encapsulate richer visual information for preserving the model's visual understanding ability. Global Semantic-guided Weight Allocator (GSWA) module allocates weights to sub-images based on their relative information density. SleighVL, a lightweight yet high-performing model, outperforms models with comparable parameters and remains competitive with larger models.
  arXiv  Detail & Related papers  (2025-01-24T06:42:06Z)
• Enhanced Multimodal RAG-LLM for Accurate Visual Question Answering [10.505845766495128]
  Multimodal large language models (MLLMs) have made significant progress in integrating visual and textual modalities. We propose a novel framework based on multimodal retrieval-augmented generation (RAG) RAG introduces structured scene graphs to enhance object recognition, relationship identification, and spatial understanding within images.
  arXiv  Detail & Related papers  (2024-12-30T13:16:08Z)
• Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models [50.98559225639266]
  We investigate the contributions of visual features from different encoder layers using 18 benchmarks spanning 6 task categories. Our findings reveal that multilayer features provide complementary strengths with varying task dependencies, and uniform fusion leads to suboptimal performance. We propose the instruction-guided vision aggregator, a module that dynamically integrates multi-layer visual features based on textual instructions.
  arXiv  Detail & Related papers  (2024-12-26T05:41:31Z)
• MMFuser: Multimodal Multi-Layer Feature Fuser for Fine-Grained Vision-Language Understanding [39.68348330596116]
  We propose modelname, a simple yet effective multi-layer feature fuser that efficiently integrates deep and shallow features from Vision Transformers (ViTs) Specifically, it leverages semantically aligned deep features as queries to dynamically extract missing details from shallow features. modelnameachieves significant improvements in visual representation and benchmark performance.
  arXiv  Detail & Related papers  (2024-10-15T17:55:22Z)
• MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct [148.39859547619156]
  We propose MMEvol, a novel multimodal instruction data evolution framework. MMEvol iteratively improves data quality through a refined combination of fine-grained perception, cognitive reasoning, and interaction evolution. Our approach reaches state-of-the-art (SOTA) performance in nine tasks using significantly less data compared to state-of-the-art models.
  arXiv  Detail & Related papers  (2024-09-09T17:44:00Z)
• Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge [76.45868419402265]
  multimodal large language models (MLLMs) have made significant strides by training on vast high-quality image-text datasets. However, the inherent difficulty in explicitly conveying fine-grained or spatially dense information in text, such as masks, poses a challenge for MLLMs. This paper proposes a new visual prompt approach to integrate fine-grained external knowledge, gleaned from specialized vision models, into MLLMs.
  arXiv  Detail & Related papers  (2024-07-05T17:43:30Z)
• MG-LLaVA: Towards Multi-Granularity Visual Instruction Tuning [44.497776004372724]
  Multi-modal large language models (MLLMs) have made significant strides in various visual understanding tasks. We present MG-LLaVA, an innovative MLLM that enhances the model's visual processing capabilities by incorporating a multi-granularity vision flow. To further refine the model's object recognition abilities, we incorporate object-level features derived from bounding boxes identified by offline detectors.
  arXiv  Detail & Related papers  (2024-06-25T17:55:11Z)
• Incorporating Visual Experts to Resolve the Information Loss in Multimodal Large Language Models [121.83413400686139]
  This paper proposes to improve the visual perception ability of MLLMs through a mixture-of-experts knowledge enhancement mechanism. We introduce a novel method that incorporates multi-task encoders and visual tools into the existing MLLMs training and inference pipeline.
  arXiv  Detail & Related papers  (2024-01-06T02:02:34Z)
• u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model [17.3535277338312]
  u-LLaVA is an innovative unifying multi-task framework that integrates pixel, regional, and global features to refine the perceptual faculties of MLLMs. This work contributes a novel mask-based multi-task dataset comprising 277K samples, crafted to challenge and assess the fine-grained perception capabilities of MLLMs.
  arXiv  Detail & Related papers  (2023-11-09T13:18:27Z)
• Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation [68.13453771001522]
  We propose a multimodal intensive ZSL framework that matches regions of images with corresponding semantic embeddings. We conduct extensive experiments and evaluate our model on large-scale real-world data.
  arXiv  Detail & Related papers  (2023-06-14T13:07:48Z)

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.