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RegionGPT: Towards Region Understanding Vision Language Model

Qiushan Guo, Shalini De Mello, Hongxu Yin, Wonmin Byeon, Ka Chun Cheung, Yizhou Yu, Ping Luo, Sifei Liu

arXiv:2403.02330v1cs.CV

TL;DR

VLMs can interpret whole images but struggle with detailed regional understanding, where spatial information and region-specific captions are important. RGPT combines region-level visual features, task-guided prompts, and generated detailed captions; the authors report impressive performance on region-level understanding tasks.

  • Problem

    VLMs struggle with detailed region understanding, while textual coordinates can leave positional interpretation to the decoder and existing captions can lack region-specific detail.

    Source: §1 · pp. 1–2

  • Method

    RGPT combines refined visual features and region pooling with task-guided prompts and a GPT-assisted pipeline for generating detailed region captions.

    Source: §1 · p. 2, §3 · p. 3

  • Results

    The authors report that RGPT achieves impressive performance on region-level understanding tasks.

    Source: §5 · p. 8

  • Takeaways & Limitations

    RGPT is presented as a model for instruction-following region understanding, including classification and identifying regions from query descriptions.

    Source: §3 · p. 3

    Task-guided prompts sometimes fail to constrain response format, so region-classification outputs may be synonyms of the ground-truth class name.

    Source: D. Limitations and Future Work · p. 14

Abstract

from arXiv · show

Vision language models (VLMs) have experienced rapid advancements through the integration of large language models (LLMs) with image-text pairs, yet they struggle with detailed regional visual understanding due to limited spatial awareness of the vision encoder, and the use of coarse-grained training data that lacks detailed, region-specific captions. To address this, we introduce RegionGPT (short as RGPT), a novel framework designed for complex region-level captioning and understanding. RGPT enhances the spatial awareness of regional representation with simple yet effective modifications to existing visual encoders in VLMs. We further improve performance on tasks requiring a specific output scope by integrating task-guided instruction prompts during both training and inference phases, while maintaining the model's versatility for general-purpose tasks. Additionally, we develop an automated region caption data generation pipeline, enriching the training set with detailed region-level captions. We demonstrate that a universal RGPT model can be effectively applied and significantly enhancing performance across a range of region-level tasks, including but not limited to complex region descriptions, reasoning, object classification, and referring expressions comprehension.

1. Introduction

VLMs handle whole images more effectively than detailed regions, although fine-grained understanding matters for attributes and object relations. RGPT addresses region-level understanding with visual feature refinement and pooling, task-guided prompts, and richer automatically generated captions.

1. Introduction

  • VLMs remain less effective at analyzing specific regions in detail, despite the importance of fine-grained attributes and inter-object relations.The introduction contrasts their image-level effectiveness with the needs of advanced vision tasks.Source: §1 · p. 1
  • Textual region coordinates leave position interpretation to the language decoder, overlooking positional information already provided by the visual encoder.The paper identifies this as a gap in integrating visual cues with linguistic context.Source: §1 · p. 1
  • RoI-aligned spatial boxes still restrict region input to boxes, leaving expressive region-specific visual representations under-explored.The paper describes such representations as potentially offering finer-grained details for downstream tasks.Source: §1 · p. 2
  • RGPT refines CLIP visual features and uses Mask Pooling to represent regions of any shape for spatial-aware semantic understanding.The paper reports that these changes significantly enhance language-model performance on spatial-aware semantic concepts.Source: §1 · p. 2
  • Task-guided prompts specify output formats and cast tasks such as closed-set classification and referring expression comprehension as VQA.The contributions describe this format specification as reducing ambiguity in responses.Source: §1 · p. 2
  • A two-stage GPT-assisted pipeline reformats object-detection data into detailed region captions averaging 87.14 words, versus 8.46 in ReferCOCOg.The paper says existing region-caption datasets often lack detailed attributes and spatial relations with surroundings.Source: §1 · p. 2

2. Related Work

Related work traces the development of language models and their extension to multimodal interaction, then reviews region-aware VLMs. The paper positions RGPT as supporting any-shape regions and task-guided outputs.

2.1. Large Language Model

  • LLMs developed from early language models to scaled systems, with capabilities including language generation, in-context learning, world knowledge, and reasoning.The cited examples include BERT, GPT-3, T5, PaLM, ChatGPT, Llama, and Vicuna.Source: §2.1 · p. 2

2.2. Multimodal Large Language Model

  • Multimodal LLM work connects visual backbones to language decoders through image-text instruction tuning, including systems for captioning and visual question answering.The passage names Flamingo, BLIP-2, LLaVA, and MiniGPT4 among early efforts.Source: §2.2 · p. 2

2.3. Region-level Vision Language Model

  • Region-aware models using textual region inputs rely on the decoder to interpret position, while box-based RoI features remain limited to boxes.RGPT instead supports any-shape regions and uses task-guided prompts to transform vision tasks into VQA tasks.Source: §2.3 · pp. 2–3

3. Method

RGPT combines region-aware visual features, task-guided instructions, and detailed region-caption data for region- and image-level understanding. Its method refines CLIP features for arbitrary-shaped regions, trains on image- and region-level tasks, and generates region captions using global context and class names.

3.1. Model Architecture

  • RGPT refines frozen CLIP ViT-L features with two stride-2 deconvolution layers, then uses Mask Pooling to extract features from arbitrary-shaped regions.The refinement upsamples the feature map by 4×; an MLP projects visual features into the language model’s embedding space.Source: §3.1 · p. 3

3.2. Region-level Instruction Tuning

  • Task-guided prompts specify response formats for vision tasks, and the paper reports improved mAP and accuracy on COCO 2017 val.Pre-training combines image captioning with regional relationship, captioning, and classification tasks; fine-tuning adds GPT-assisted captions and prompts for classification and referring-expression comprehension.Source: Table 1 · p. 4, §3.2 · pp. 4–5

3.3. GPT-assisted Region Caption Generation

  • RecapD provides region-focused descriptions of attributes such as color, shape, material, and spatial relationships, unlike datasets with holistic image descriptions.The pipeline annotates 213K V3Det images and uses CLIP similarity to filter captions.Source: §3.3 · pp. 5–6
  • The caption-generation pipeline first produces a global image caption, then prompts a vision-language model with a cropped region and its class name to describe that region.The class name is added because the two-stage approach alone can misidentify objects in cropped regions.Source: §3.3 · p. 6
  • 87.14 words per caption is the reported average for RGPT’s region-caption dataset, higher than other compared datasets.The pipeline filters generated captions using CLIP similarity to remove noisy or irrelevant samples.Source: §3.3 · p. 6, Table 2 · p. 6

4. Experiments

RGPT is evaluated on region classification, captioning, expression comprehension, and object hallucination, with quantitative and qualitative results. The reported findings cover task performance, feature and backbone ablations, and multi-region reasoning.

4. Experiments

  • Experiments cover region classification, captioning, expression comprehension, and object hallucination; the visual backbone stays frozen during training.Training uses 336×336 images in pre-training and fine-tuning.Source: §4 · p. 6, §4.1 · p. 6

4.2. Quantitative Evaluation

  • 70.0 mAP and 80.86% accuracy on COCO-2017 show RGPT’s region-level object recognition performance.The evaluation uses ground-truth boxes and task-guided prompts; the focus is region recognition rather than localization.Source: §4.2 · p. 7
  • RGPT surpasses the region-aware VLM Kosmos-2 on region-level captioning on ReferCOCOg and Visual Genome.The evaluation reports METEOR and CIDEr, following image-level caption evaluation.Source: §4.2 · p. 7, Table 4 · p. 7
  • The two-deconvolution-layer feature refinement significantly outperforms the baseline; 16x upsampling improves small-object accuracy but reduces large-object performance.The authors describe their approach as a superior trade-off between these object-size effects.Source: §4.2 · p. 7, Table 6 · p. 7
  • RGPT’s ReferCOCOg results highlight its ability to interpret complex expressions within provided regions, rather than localize objects.Candidate box sets are used, with ground-truth boxes added when no candidate reaches IoU 0.5.Source: §4.2 · p. 7, Table 5 · p. 7
  • Using a more powerful visual backbone can further improve RGPT’s region-level understanding performance.The backbone ablation reports object classification on COCO-2017 validation.Source: Table 7 · p. 7, §4.2 · p. 8
  • RGPT significantly outperforms recent popular image-level VLMs on the POPE object-hallucination benchmark.The authors attribute the gain over the LLaVA baseline to region-level instruction fine-tuning.Source: §4.2 · p. 8, Table 8 · p. 8

4.3. Qualitative Evaluation

  • RGPT analyzes relationships between multiple image regions and demonstrates complex region-level reasoning in qualitative examples.The figure caption also reports preserved multi-turn conversation and image-level captioning ability.Source: §4.3 · p. 8

5. Conclusion

RGPT is a general vision-language model for instruction-following region-level captioning and reasoning. It combines region-level instruction tuning, task-guided prompts, and GPT-assisted data reformation, with reported strong region-understanding performance.

5. Conclusion

  • RGPT is a general vision-language model for complex region-level captioning and reasoning following user instructions.Region-level instruction tuning aligns visual features with the language word-embedding space.Source: §5 · p. 8
  • Task-guided prompts convert vision tasks to VQA tasks and specify response formats, while a two-stage GPT-assisted pipeline creates detailed region-level captions.The pipeline reformats object-detection data.Source: §5 · p. 8
  • The paper reports impressive region-level understanding performance while preserving multi-turn conversation and image-level captioning ability.Source: §5 · p. 8, Figure 4 · p. 8

A. Data

The appendix describes instruction formats for region-level tasks, including descriptions, relationships, classification, and referring-expression comprehension.

A. Data

  • Brief and detailed region descriptions use separate instruction sets, and relationship descriptions have their own prompts.Source: §A.1 · p. 12
  • Region classification has dedicated instructions, while referring-expression comprehension is framed as selecting matching regions from choices.Source: §A.1 · p. 12

A.2. Instruction Tuning Data.

The appendix describes region-level instruction-tuning data across pre-training and fine-tuning, including annotation selection and multi-task conversation formatting.

A.2. Instruction Tuning Data.

  • Multiple-task datasets are combined into multi-turn conversations to improve training efficiency and support multi-round dialogue.Source: §A.2 · p. 12
  • For each category, annotations are randomly selected up to a target count, and images with no selected annotations are discarded.Source: §A.2 · p. 12
  • The appendix identifies separate region-level training-data tables for pre-training and fine-tuning.Source: Table 9 · p. 12, Table 10 · p. 12

B. More Ablation Studies

The ablations compare three region-classification instruction modes and test how annotation quantity affects performance. Multi-turn classification performs best among the modes, while gains diminish as annotations increase.

B. More Ablation Studies

  • The compared instruction modes are one-turn for all RoIs, multi-turn for all RoIs, and one-turn for one RoI per dialogue.Source: B. More Ablation Studies · p. 12
  • Multi-turn conversation outperforms the other instruction modes on COCO 2017 val region classification.The comparison uses ViT-B/16 and all COCO region instances without random sampling.Source: B. More Ablation Studies · p. 12, Table 11 · p. 12
  • Increasing annotations from 20 to 200 per category yields only a 4 mAP increase, with marginal gains diminishing as data grows.The annotation-quantity experiment uses COCO classification region-level data only.Source: B. More Ablation Studies · p. 12, Table 12 · p. 12

C. More Qualitative Results

The qualitative examples illustrate RGPT’s region classification, relationship inference, and multi-region reasoning, including detailed descriptions and identification among similar objects.

C. More Qualitative Results

D. Limitations and Future Work

The section identifies two boundaries: task-guided prompts may not enforce the requested classification format, and filtering does not rule out potential bias in the language model’s responses.

D. Limitations and Future Work

  • Task-guided prompts sometimes fail to restrict classification responses to the requested format, so evaluation may use text-encoder semantic similarity to handle synonyms.Source: D. Limitations and Future Work · p. 14

E. Ethics Concerns

  • Despite filtering, the language model’s responses may retain potential bias from its pretraining corpus, which was collected by previous works.Source: E. Ethics Concerns · p. 14
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