Hidden Meanings in Plain Sight: RebusBench for Evaluating Cognitive Visual Reasoning
RebusBench evaluates LVLMs' cognitive visual reasoning, performance below 10% exact match.
Key Findings
Methodology
The paper evaluates LVLMs' cognitive visual reasoning using RebusBench, comprising 1,164 puzzles requiring models to integrate visual and linguistic features for abstract mapping. Experiments with models like Qwen, InternVL, and LLaVA show poor performance in exact match and semantic accuracy.
Key Results
- Result 1: On RebusBench, the Qwen 2.5-32B model's exact match rate is below 10%, semantic accuracy below 20%.
- Result 2: Despite increasing model parameters and using few-shot learning, performance improvements are limited.
- Result 3: Semantic evaluation using GPT-4o remains below 20%.
Significance
This study reveals the shortcomings of current LVLMs in integrating visual and linguistic features, emphasizing the need for stronger cognitive reasoning abilities to solve complex visual puzzles. This is significant for advancing vision-language model development.
Technical Contribution
RebusBench provides a new benchmark focusing on evaluating the integration of visual and linguistic features, filling the gap in existing evaluation methods. It highlights the limitations of current models in abstract reasoning.
Novelty
RebusBench is the first benchmark specifically designed to evaluate cognitive reasoning capabilities of vision-language models, differing from traditional visual question answering datasets by emphasizing deep integration of visual and linguistic features.
Limitations
- Limitation 1: Current models perform poorly in complex visual puzzles, especially those requiring multi-step reasoning.
- Limitation 2: Increasing model parameters does not significantly improve performance, indicating computational power alone cannot solve cognitive deficiencies.
Future Work
Future research will expand dataset diversity, add fine-grained metadata to diagnose modality biases, and evaluate the performance of proprietary closed-source models.
AI Executive Summary
Large Vision-Language Models (LVLMs) have made significant progress in visual recognition but still exhibit cognitive deficiencies when handling complex visual puzzles. Existing datasets like VQA v2 and CLEVR primarily assess rapid perceptual abilities rather than deep reasoning. To address this, the paper introduces RebusBench, a benchmark comprising 1,164 puzzles aimed at evaluating cognitive reasoning capabilities of models. Experimental results show that even state-of-the-art models like Qwen and InternVL perform poorly in exact match and semantic accuracy, failing to break the 10% and 20% barriers. This indicates that current models lack the ability to integrate visual and linguistic features into abstract concepts. Future research will expand dataset diversity and evaluate closed-source models to advance vision-language model development.
Deep Analysis
Background
Vision-language models have made significant progress recently, particularly in visual question answering and spatial grounding. Representative works include Flamingo and BLIP, which set new standards in visual recognition and semantic understanding. However, these models still struggle with complex visual puzzles requiring multi-step reasoning.
Core Problem
Existing models perform poorly in solving complex visual puzzles, especially those requiring multi-step reasoning and integration of visual and linguistic features. This cognitive deficiency limits the potential of models in real-world applications.
Innovation
RebusBench fills the gap in existing evaluation methods by assessing cognitive reasoning capabilities of vision-language models. It emphasizes deep integration of visual and linguistic features, differing from traditional visual question answering datasets.
Methodology
- �� Evaluate LVLMs' cognitive reasoning using RebusBench
- �� Dataset comprises 1,164 puzzles requiring abstract mapping of visual and linguistic features
- �� Experiments conducted with models like Qwen, InternVL, and LLaVA
- �� Assess exact match and semantic accuracy
Experiments
Experimental design includes evaluating models like Qwen, InternVL, and LLaVA using the RebusBench dataset. Evaluation metrics include exact match rate and semantic accuracy. Experiments also test the impact of increasing model parameters and few-shot learning on performance.
Results
Experimental results show that even state-of-the-art models like Qwen and InternVL perform poorly in exact match and semantic accuracy, failing to break the 10% and 20% barriers. This indicates that current models lack the ability to integrate visual and linguistic features into abstract concepts.
Applications
RebusBench can be used to evaluate cognitive reasoning capabilities of vision-language models, helping researchers identify model deficiencies and advance model development.
Limitations & Outlook
Current models perform poorly in complex visual puzzles, especially those requiring multi-step reasoning. Increasing model parameters does not significantly improve performance, indicating computational power alone cannot solve cognitive deficiencies.
Plain Language Accessible to non-experts
Imagine you're playing a visual puzzle game. Each puzzle is an image with some text and symbols. Your task is to find a hidden phrase or word using these clues. For example, a red letter E next to two 'GO's means 'ready to go'. These puzzles require you to integrate visual and linguistic features for reasoning, not just describe what's in the image. It's like piecing together a puzzle to form a complete picture.
ELI14 Explained like you're 14
Hey, buddy! Imagine you're playing a super cool puzzle game. Each puzzle is a picture with some text and symbols. Your task is to find a hidden phrase or word using these clues. For example, a red letter E next to two 'GO's means 'ready to go'. These puzzles require you to use your brain, integrating visual and linguistic features for reasoning, not just describing what's in the picture. It's like piecing together a puzzle to form a complete picture. Isn't that fun?
Glossary
Vision-Language Model
A model that processes both visual and linguistic features, capable of understanding text and symbols in images.
Used to evaluate cognitive reasoning capabilities.
Cognitive Reasoning
The ability to integrate visual and linguistic features for multi-step reasoning.
Key capability in solving complex visual puzzles.
RebusBench
A benchmark designed to evaluate cognitive reasoning capabilities of vision-language models, comprising 1,164 puzzles.
Used to test integration of visual and linguistic features.
Exact Match
Evaluation of how closely model predictions match the true answer.
Used to assess model performance on RebusBench.
Semantic Accuracy
Evaluation of semantic similarity between model predictions and true answers.
Used to assess model performance on RebusBench.
Open Questions Unanswered questions from this research
- 1 Current models perform poorly in complex visual puzzles, especially those requiring multi-step reasoning.
- 2 Increasing model parameters does not significantly improve performance, indicating computational power alone cannot solve cognitive deficiencies.
Applications
Immediate Applications
Vision-Language Model Evaluation
RebusBench can be used to evaluate cognitive reasoning capabilities of vision-language models, helping researchers identify model deficiencies.
Long-term Vision
Cognitive Reasoning Enhancement
By expanding datasets and evaluating closed-source models, advance vision-language model development.
Abstract
Large Vision-Language Models (LVLMs) have achieved remarkable proficiency in explicit visual recognition, effectively describing what is directly visible in an image. However, a critical cognitive gap emerges when the visual input serves only as a clue rather than the answer. We identify that current models struggle with the complex, multi-step reasoning required to solve problems where information is not explicitly depicted. Successfully solving a rebus puzzle requires a distinct cognitive workflow: the model must extract visual and textual attributes, retrieve linguistic prior knowledge (such as idioms), and perform abstract mapping to synthesize these elements into a meaning that exists outside the pixel space. To evaluate this neurosymbolic capability, we introduce RebusBench, a benchmark of 1,164 puzzles designed to test this specific integration of perception and knowledge. Our evaluation of state-of-the-art models (including Qwen, InternVL, and LLaVA) shows a severe deficiency: performance saturates below 10% Exact Match and 20% semantic accuracy, with no significant improvement observed from model scaling or In-Context Learning (ICL). These findings suggest that while models possess the necessary visual and linguistic components, they lack the cognitive reasoning glue to connect them. Project page available at https://amirkasaei.com/rebusbench/.