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ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
Mu Cai, Haotian Liu, Dennis Park, Siva Karthik Mustikovela, Gregory P. Meyer, Yuning Chai, Yong Jae Lee
TL;DR
Existing multimodal models and spatial references provide limited region-specific comprehension or user-friendly visual interaction. ViP-LLaVA directly overlays arbitrary visual prompts and processes them with multimodal features, achieving state-of-the-art region-understanding results while introducing ViP-Bench for broader evaluation.
Problem
Current multimodal models focus mainly on whole-image understanding, while existing coordinate-based or spatial references often provide limited user-friendly interaction for region-specific comprehension.
Method
ViP-LLaVA overlays arbitrary visual markers onto the image and uses the resulting visual features with text instructions to generate conversational responses.
Results
ViP-LLaVA achieves state-of-the-art results on region-understanding tasks and outperforms specialized region-encoding models, while ViP-Bench evaluates six region-level capabilities.
Takeaways & Limitations
Direct visual overlays provide an intuitive interface for region comprehension and support a benchmark for assessing arbitrary visual-prompt understanding.
Takeaways & Limitations
Region-level models still struggle with mathematics, relationship reasoning, and language generation, suggesting potential overfitting to existing public region-level datasets.
Abstract
from arXiv · showhide
While existing large vision-language multimodal models focus on whole image understanding, there is a prominent gap in achieving region-specific comprehension. Current approaches that use textual coordinates or spatial encodings often fail to provide a user-friendly interface for visual prompting. To address this challenge, we introduce a novel multimodal model capable of decoding arbitrary visual prompts. This allows users to intuitively mark images and interact with the model using natural cues like a "red bounding box" or "pointed arrow". Our simple design directly overlays visual markers onto the RGB image, eliminating the need for complex region encodings, yet achieves state-of-the-art performance on region-understanding tasks like Visual7W, PointQA, and Visual Commonsense Reasoning benchmark. Furthermore, we present ViP-Bench, a comprehensive benchmark to assess the capability of models in understanding visual prompts across multiple dimensions, enabling future research in this domain. Code, data, and model are publicly available.
1. Introduction
Existing multimodal models largely understand whole images rather than specific regions, while coordinate-based references offer limited usability. ViP-LLaVA addresses this gap by interpreting arbitrary visual prompts overlaid on images and introduces ViP-Bench for region-level evaluation.
- Current multimodal models focus predominantly on whole-image understanding and lack region-specific comprehension in complex scenes.
- Language-only descriptions become ambiguous when multiple similar objects appear and a question targets a specific one.
- Prior spatial-reference methods use coordinates, positional embeddings, or ROI features but often restrict users to fixed-format references such as bounding boxes.
- ViP-LLaVA directly overlays arbitrary visual prompts, including red bounding boxes and pointed arrows, onto images for conversational interaction.
- ViP-Bench evaluates region understanding across recognition, OCR, knowledge, math, object relationships, and language generation using 303 images and questions.
- ViP-LLaVA is reported to achieve state-of-the-art region-understanding results while simplifying visual referencing through direct image overlays.
2. Related Work
Related work has advanced multimodal perception but remains constrained in region-specific interaction and evaluation. ViP-LLaVA is situated among efforts to make visual prompting more natural and region-level assessment more comprehensive.
- Open-source vision-language models combine language models with image encoders and have made substantial progress in whole-image understanding.
- Region-specific comprehension has motivated textual coordinates, learned positional embeddings, and ROI features to connect language with image regions.
- Visual prompting research seeks more natural interaction by expanding beyond regular shapes to markers such as points and scribbles.
- Existing region-understanding evaluations emphasize multichoice or captioning metrics, which are limited for open-world visual dialogue.
3. Approach
ViP-LLaVA overlays arbitrary markers on images and uses CLIP-derived multilevel features with language instructions to generate responses. Its training combines prompted and unprompted instruction data with diverse synthetic visual cues.
- Visual Prompt Embedding via CLIP: The model alpha-blends visual prompts onto the original image before encoding the composite image with CLIP.
- Visual Prompt Embedding via CLIP: CLIP features from one early layer and four deeper layers are concatenated, normalized, and projected through an MLP to balance geometric and semantic information.
- Visual Prompting Design: The direct-overlay design avoids additional region-processing modules while supporting diverse user-generated visual cues.
- Visual Prompt Embedding via CLIP: Visual and text instruction tokens are trained autoregressively so responses condition on image content, prompts, instructions, and preceding answer tokens.
- Instruction Tuning Data: The visual prompt instruction dataset contains 520k marked image-text pairs spanning single-, two-, and multi-region reasoning.
- Visual Prompting Design: Prompts include rectangles, ellipses, points, triangles, masks, mask contours, arrows, and scribbles, with varied visual properties.
- Instruction Tuning Data: Training mixes region-level prompting data with image-level LLaVA data to retain human-like multimodal conversation abilities.
4. ViP-Bench for Evaluation
ViP-Bench is a 303-pair benchmark for region-level visual reasoning across six capabilities, using human validation and GPT-4 scoring. It is designed as a challenging testbed for comparing multimodal models.
- ViP-Bench contains 303 image-question pairs drawn from MM-Vet, MMBench, and Visual Genome for region-understanding evaluation.
- The benchmark covers recognition, OCR, knowledge, math, object relationship reasoning, and language generation at the region level.
- Responses are scored from 0 to 10 by GPT-4 using the model response, human answer, and in-context scoring examples.
- Seven validation rounds assess the accuracy and relevance of annotated object regions, questions, and answers.
- The benchmark is presented as a comprehensive and challenging testbed intended to support future advances in visual reasoning and multimodal interaction.
5. Experiments
ViP-LLaVA is evaluated on region-reasoning benchmarks and arbitrary visual-prompt capabilities, using direct image overlays and CLIP-based visual features. Across these evaluations, it achieves strong region understanding while supporting multi-region reasoning, arrows, text markers, and varied prompt attributes.
- Visual7W: ViP-LLaVA-7B outperforms recent state-of-the-art methods on Visual7W, while ViP-LLaVA-13B produces even higher gains.The comparison includes GPT4RoI and Shikra, despite ViP-LLaVA using fewer parameters.
- PointQA-LookTwice: ViP-LLaVA achieves superior performance on PointQA-LookTwice’s broad-question task, which combines regional object identification with category-specific counting.The overlaid bounding box keeps the object unobscured while connecting the queried region to the original image.
- Visual Commonsense Reasoning: ViP-LLaVA attains state-of-the-art validation performance on VCR after fine-tuning, demonstrating visual commonsense and multi-region reasoning.The model must select answers and provide rationales for questions grounded in visual scenes.
- In-depth Analysis: ViP-LLaVA generalizes to human-drawn regions, arrow direction, text-marker OCR and correspondence reasoning, and prompt variations in thickness or location.The model was trained on eight prompt types, while some tested variations were not explicitly seen during training.
- Ablation Studies: 81.63% accuracy with original and overlaid image tokens versus 82.47% with overlaid tokens only suggests that visual overlays do not detract from processed visual information.This comparison evaluates the VCR validation split.
- Ablation Studies: Multi-layer CLIP features improve performance, particularly when multiple visual prompts must be localized and recognized in Visual7W and VCR.The ablation contrasts multi-layer features with single-layer features from CLIP’s second-last layer.
6. ViP-Bench Evaluation Results
ViP-Bench evaluates multimodal models across varied visual-prompt formats and region-level capabilities. ViP-LLaVA outperforms other evaluated models except GPT-4V, while results expose gaps in language-oriented tasks and possible overfitting among region-level models.
- ViP-LLaVA outperforms other evaluated models on ViP-Bench except GPT-4V.The benchmark includes image-level and region-level LMMs, and all models perform below perfect accuracy.
- Visual prompts outperform simple textual coordinate representations in zero-shot evaluation for models such as Qwen-VL and LLaVA.The comparison uses a list of four textual numerical values as the alternative representation.
- Open-source LMMs show a significant gap relative to GPT-4V in OCR, math, and language generation, while performing decently in recognition, knowledge, and relationship reasoning.
- The results indicate that OCR performance needs higher-resolution inputs or a more robust backbone beyond existing CLIP capabilities.
- Region-level LMMs including Shikra, GPT4ROI, and Kosmos-2 struggle with mathematics, relationship reasoning, and language generation.The authors suggest these difficulties may reflect overfitting to public region-level datasets dominated by brief descriptions.
7. Conclusion
The conclusion presents visual prompting as a promising approach to region-specific image understanding. ViP-LLaVA combines natural linguistic interaction with visual markers, and the paper reports state-of-the-art performance on established benchmarks.
- Visual prompts are presented as promising for region-specific image understanding.
- ViP-LLaVA integrates arbitrary visual prompts to combine user-friendly interaction with precise region comprehension.
- The model couples natural linguistic interactions with visual markers to simplify image annotation and clarify visual references.
- The paper reports state-of-the-art performance on established benchmarks.
- The authors believe ViP-LLaVA can motivate more sophisticated integration of visual and linguistic modalities for human-machine interactions.
Supplementary Material
The supplementary material examines prompt generalization, instruction-data curation, arrow-direction understanding, prompt-type effects, and qualitative region-understanding behavior. Across these analyses, ViP-LLaVA handles diverse prompts and benefits from additional GPT-4V instruction data.
- Prompt generalization: ViP-LLaVA generalizes across eight visual-prompt types, including contours, ellipses, boxes, triangles, scribbles, points, arrows, and masks.The supplementary material also reports handling varying prompt thicknesses and diverse markers not explicitly seen during training.
- Qualitative examples: The model localizes digit-tagged objects and generates precise descriptions, recognizes counterfactual marker colors, and localizes objects in densely marked scenes.
- Instruction-data curation: GPT-4V region-level instruction data is combined with 13K original entries to form a 26K-entry stage 3 fine-tuning dataset.The fine-tuned model improves across nearly all datasets for both 7B and 13B models.
- Instruction-data curation: Without GPT-4V instruction data, ViP-LLaVA still outperforms contemporary methods on Visual7W, PointQA-LookTwice, and ViP-Bench.Adding GPT-4V instruction data further amplifies this performance advantage.
- Arrow-direction understanding: ViP-LLaVA-13B achieves 90.28% accuracy on the constructed arrow-direction dataset.The dataset contains 3,520 paired examples in which arrows point between object centers.
- Prompt-type effects: The 7B model maintains consistent accuracy across varied visual prompts on VCR, Visual7W, PointQA, and ViP-Bench.Point and Ellipse prompts achieve marginally better performance than the others.
A.5. Impact of Different LLMs on Image-level and Region-level Benchmarks
The supplementary analysis compares language-model backbones for LLaVA-1.5 and ViP-LLaVA on whole-image and region-level benchmarks. Larger-token-trained Llama-3 and Phi-3 improve language understanding and reasoning, but do not consistently improve visually dominated tasks.
- Experimental setup: The study compares Vicuna-1.5-7B, Vicuna-1.5-13B, Llama-3-8B, and Phi-3-mini-3.8B as language-model backbones under matched configurations.
- Experimental setup: The analysis covers both whole-image and region-level vision-language benchmarks.The benchmark list includes image-level tasks and region-level tasks such as Visual7W and PointQA-LookTwice.
- Backbone comparison: Llama-3-8B and Phi-3-mini-3.8B use fewer parameters but were trained on 15T and 3.3T tokens, respectively.The passage links these training differences with significantly improved language understanding and reasoning capability.
- Backbone comparison: Llama-3-8B and Phi-3-mini-3.8B do not improve tasks primarily requiring visual understanding rather than language reasoning.Vicuna-1.5-13B still performs better on examples including MME and TextVQA.
B. Training Details
ViP-LLaVA training uses a staged procedure that first trains the multimodal connector, then jointly adapts language-side components while keeping the image encoder frozen.
- Training comprises MLP-projector pretraining with frozen language and image encoders, followed by instruction tuning and further fine-tuning.
C. Additional Ablation Studies
Additional ablations compare ViP-LLaVA with matched region-encoding settings, coordinate-based prompts, and separated image inputs to test the source of its performance.
- ViP-LLaVA consistently outperforms GPT4ROI on ViP-Bench and achieves comparable Visual7W results under matched encoder, resolution, and language-model settings.The comparison uses CLIP ViT-L, 224-pixel inputs, and Vicuna v1.1.
- Visual prompts significantly outperform textual coordinate formats on PointQA-LookTwice and ViP-Bench@Box, while Visual7W performance remains comparable.The 7B models use identical training data and schedules.
- Separating the source image from the overlaid cue doubles visual tokens, yet the two input formats perform comparably.Both settings are trained as 7B models.
D. Additional Experimental Results
Further experiments examine region captioning, test-time prompt augmentation, benchmark composition, evaluation consistency, and qualitative failures of competing models.
- ViP-LLaVA-Base-7B demonstrates strong region-captioning performance on RefCOCOg using CIDEr and METEOR.The model is fine-tuned for one epoch after stage 2 training.
- GPT-4 judge scores vary by only 0.1 across five assessments, indicating stable evaluation of ViP-LLaVA-Base-7B.
- Visual prompt augmentation improves ViP-LLaVA-Base-7B performance by aggregating predictions from varied visual prompt formulations.The method can replace a red rectangle with a red scribble and update the overlaid image accordingly.
- ViP-Bench emphasizes recognition and includes 89 OCR examples across capabilities that may overlap within samples.Its capability proportions can sum to more than 100% because some samples require multiple capabilities.
- ViP-Bench includes synthesized tight bounding boxes and diverse human-annotated prompts, covering both single-region and multi-region analysis.
- Qualitative cases show GPT-4V and LLaVA-1.5 misinterpreting visual markers, whereas ViP-LLaVA answers the corresponding questions correctly.Reported examples include confusion over a yellow scribble and a person marked by a pink point.