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Visual Structures Help Visual Reasoning: Addressing the Binding Problem in LVLMs
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Proposes VISER, which overlays horizontal lines with row labels on images and adds a sequential-scanning prompt, improving LVLM binding-related performance on counting, visual search, scene description and spatial reasoning in a single query.
Problem
LVLMs suffer from the binding problem, failing to associate perceptual features with the correct objects, which causes errors in counting, visual search, scene description, and spatial relation reasoning.
Approach
Augment the input image with a few equidistant horizontal lines labeled top to bottom, paired with a textual prompt to scan sequentially row by row, injecting a spatial serial-attention inductive bias into the visual input without fine-tuning or multiple queries.
Key results
- VISER improves GPT-4o visual search, counting and spatial relation accuracy by 25.0%, 26.8% and 9.5% on 2D synthetic scenes.
- On 2D counting, Qwen2.5-VL average accuracy rises from 5.83% to 40.83% with VISER.
- Across 120 pairwise comparisons, VISER outperforms baseline in 103 cases (sign test p = 7.4e-15 on non-tied cases).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add VISER visual structure and sequential scanning prompt | GPT-4o · 2D counting (Avg) | accuracy (%): 12.00 -> 38.83 | Helped |
| Add VISER visual structure and sequential scanning prompt | Qwen2.5-VL · 2D counting (Avg) | accuracy (%): 5.83 -> 40.83 | Helped |
| Add VISER visual structure and sequential scanning prompt | GPT-4o · 3D counting (Avg) | accuracy (%): 15.00 -> 31.00 | Helped |
| Add VISER visual structure and sequential scanning prompt | Qwen2.5-VL · Natural counting | accuracy (%): 18.91 -> 17.29 | Hurt |
| Add VISER visual structure and sequential scanning prompt | GPT-4o · 2D visual search (Avg) | harmonic mean: 0.48 -> 0.73 | Helped |
| Add VISER visual structure and sequential scanning prompt | Claude3.5-sonnet · 2D visual search (Avg) | harmonic mean: 0.34 -> 0.66 | Helped |
| Add VISER visual structure and sequential scanning prompt | GPT-4o · 3D visual search (Avg) | harmonic mean: 0.91 -> 0.93 | Helped |
| Add VISER visual structure and sequential scanning prompt | LLaMa4 · 3D visual search (Avg) | harmonic mean: 0.56 -> 0.59 | Mixed |
| Use horizontal rows instead of no visual structure (ablation) | GPT-4o · 2D synthetic counting | accuracy: 16.85 -> 31.55 | Helped |
| Use grid structure instead of horizontal rows (ablation) | GPT-4o · 2D synthetic spatial relations | accuracy: 39.00 -> 51.50 | Helped |
| Remove numerical row labels from horizontal lines (ablation) | GPT-4o · 2D synthetic counting | accuracy: 31.55 -> 19.14 | Hurt |
| Prompt for imaginary lines without drawing them, instead of VISER rendered lines (ablation) | GPT-4o · 2D visual search | harmonic mean accuracy: 68.00 -> 73.00 | Hurt |
Surprising findings
- Chain-of-Thought prompting can degrade performance on visual tasks relative to baseline, while the visual scaffold helps.
- Claude3.5-sonnet counting improvements on 2D scenes are small, and Qwen2.5-VL drops slightly on natural counting (18.91% to 17.29%).
Limitations
- Static fixed scaffold may fail when lines interfere with objects or objects cluster.
- Gains on natural images are modest and inconsistent (e.g., Qwen2.5-VL counting drop, LLaMa4 spatial drop on 2D).
Experimental setup
Datasets: Binding Problem Generator (synthetic 2D/3D scenes) Learning To Count Everything Spatial Reasoning dataset
Benchmarks: synthetic 2D/3D visual search synthetic 2D/3D counting synthetic scene description synthetic and natural spatial relationship MMBench PhysBench RAVEN Visual Analogy
Metrics: accuracy harmonic mean edit distance MSE F1 Jaccard similarity binomial sign test p-value
Baselines: Chain-of-Thought prompting Mulberry OpenVLThinker Imaginary-Baseline
Builds on: chain-of-thought prompting Binding Problem Generator
Closest papers by meaning
- Why Is Spatial Reasoning Hard for VLMs? An Attention Mechanism Perspective on Focus Areas (ICML 2025 · similarity 0.93)
- Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models (NeurIPS 2024 · similarity 0.92)
- Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence (NeurIPS 2025 · similarity 0.91)
- Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing (NeurIPS 2025 · similarity 0.91)
- Linear Mechanisms for Spatiotemporal Reasoning in Vision Language Models (ICLR 2026 · similarity 0.91)
- Struct2D: A Perception-Guided Framework for Spatial Reasoning in MLLMs (NeurIPS 2025 · similarity 0.91)
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