# Exploring Perceptual Limitation of Multimodal Large Language Models

arXiv: https://arxiv.org/abs/2402.07384 · HTML: https://mlsanity.com/paper/2402.07384/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 26 of its 36 references that are in the map.
- First posted on arXiv 12 Feb 2024.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 26 of 26)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
- [Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs](https://mlsanity.com/paper/2401.06209/) (2024)
- [CogAgent: A Visual Language Model for GUI Agents](https://mlsanity.com/paper/2312.08914/) (2023)
- [VILA: On Pre-training for Visual Language Models](https://mlsanity.com/paper/2312.07533/) (2023)
- [V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs](https://mlsanity.com/paper/2312.14135/) (2023)
- [AppAgent: Multimodal Agents as Smartphone Users](https://mlsanity.com/paper/2312.13771/) (2023)
- [RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback](https://mlsanity.com/paper/2312.00849/) (2023)
- [TextCaps: a Dataset for Image Captioning with Reading Comprehension](https://mlsanity.com/paper/2003.12462/) (2020)
- [EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought](https://mlsanity.com/paper/2305.15021/) (2023)
- [MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning](https://mlsanity.com/paper/2309.07915/) (2023)
- [Reformulating Vision-Language Foundation Models and Datasets Towards Universal Multimodal Assistants](https://mlsanity.com/paper/2310.00653/) (2023)
- [The Curious Case of Nonverbal Abstract Reasoning with Multi-Modal Large Language Models](https://mlsanity.com/paper/2401.12117/) (2024)

## Built on by (most cited first, 25 of 25)

- [MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs](https://mlsanity.com/paper/2502.17422/) (2025)
- [GUICourse: From General Vision Language Models to Versatile GUI Agents](https://mlsanity.com/paper/2406.11317/) (2024)
- [Video-Bench: Human-Aligned Video Generation Benchmark](https://mlsanity.com/paper/2504.04907/) (2025)
- [Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototyping](https://mlsanity.com/paper/2411.03292/) (2024)
- [MARVEL: Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning](https://mlsanity.com/paper/2404.13591/) (2024)
- [Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1)](https://mlsanity.com/paper/2504.03151/) (2025)
- [Efficient Multi-modal Large Language Models via Progressive Consistency Distillation](https://mlsanity.com/paper/2510.00515/) (2025)
- [Constructive Distortion: Improving MLLMs with Attention-Guided Image Warping](https://mlsanity.com/paper/2510.09741/) (2025)
- [Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing](https://mlsanity.com/paper/2409.18286/) (2024)
- [SPARC: Separating Perception And Reasoning Circuits for Test-time Scaling of VLMs](https://mlsanity.com/paper/2602.06566/) (2026)
- [Vision Language Models Cannot Reason About Physical Transformation](https://mlsanity.com/paper/2603.07109/) (2026)
- [Visual Language Models show widespread visual deficits on neuropsychological tests](https://mlsanity.com/paper/2504.10786/) (2025)
- [Egocentric Bias in Vision-Language Models](https://mlsanity.com/paper/2602.15892/) (2026)
- [Transferability Between Understanding and Generation in Unified Multimodal Models](https://mlsanity.com/paper/2607.04423/) (2026)
- [RED QUEEN: Safeguarding Large Language Models against Concealed Multi-Turn Jailbreaking](https://mlsanity.com/paper/2409.17458/) (2024)
- [MDF-MLLM: Deep Fusion Through Cross-Modal Feature Alignment for Contextually Aware Fundoscopic Image Classification](https://mlsanity.com/paper/2509.21358/) (2025)
- [Evaluating Graphical Perception with Multimodal LLMs](https://mlsanity.com/paper/2504.04221/) (2025)
- [A Study of Commonsense Reasoning over Visual Object Properties](https://mlsanity.com/paper/2508.10956/) (2025)
- [SeqBench: Benchmarking Sequential Narrative Generation in Text-to-Video Models](https://mlsanity.com/paper/2510.13042/) (2025)
- [RISE: Reliable Improvement in Self-Evolving Vision-Language Models](https://mlsanity.com/paper/2605.20914/) (2026)
- [The Percept-V Challenge: Can Multimodal LLMs Crack Simple Perception Problems?](https://mlsanity.com/paper/2508.21143/) (2025)
- [Visual Attention Drifts,but Anchors Hold:Mitigating Hallucination in Multimodal Large Language Models via Cross-Layer Visual Anchors](https://mlsanity.com/paper/2603.25088/) (2026)
- [Fine-Grained Multi Image Object Hallucination Benchmark](https://mlsanity.com/paper/2608.30653/) (2026)
- [ASAP: Visual Analytics for Identifying and Analyzing Image Patterns in AI-generated Images](https://mlsanity.com/paper/2609.27371/) (2026)
- [Human-Like Attention? A Psychophysical Comparison of Visual Search in Humans and MLLMs](https://mlsanity.com/paper/2610.05463/) (2026)

## Abstract (arXiv)

Multimodal Large Language Models (MLLMs) have recently shown remarkable perceptual capability in answering visual questions, however, little is known about the limits of their perception. In particular, while prior works have provided anecdotal evidence of MLLMs' sensitivity to object size, this phenomenon and its underlying causes have not been explored comprehensively. In this work, we quantitatively study the perception of small visual objects in several state-of-the-art MLLMs and reveal a pervasive limitation in answering questions about small objects in images. Next, we identify four independent factors that can contribute to this limitation -- object quality, size, distractors, and location -- and conduct controlled intervention studies to measure the effect of each factor on MLLMs' perception. In particular, we find that lower object quality and smaller object size can both independently reduce MLLMs' ability to answer visual questions. More surprisingly, we find that the location of the object in the image and the presence of visual distractors can also significantly reduce MLLMs' question answering accuracy. Our study provides a better understanding of the perceptual limitation of MLLMs and contributes new evaluation protocols for analyzing the perception of future MLLMs. To facilitate further investigations, we release our code and data.