Latent Noise Mask for Reducing Visual Redundancy in Multimodal Large Language Models
LatEnt Noise maSk (Lens) enhances multimodal large language models by reducing visual redundancy, improving VQA datasets by 2.4-6.4 points.
Key Findings
Methodology
Lens introduces a lightweight Lens Evidence Token (LET) to score visual tokens' relevance and injects adaptive latent noise into low-relevance tokens, suppressing distractors without altering the model backbone or token sequence. This method leverages question-relevant visual evidence for reasoning, avoiding interference from redundant visual information.
Key Results
- Lens improved base MLLM by 2.4-6.4 points on most VQA datasets and by 4.1-6.4 points on grounding tasks, demonstrating the direct benefits of cleaner visual evidence for multimodal reasoning.
- On the GQA dataset, Lens-GRPO model performance increased from 72.65 to 83.95, showing significant advantages in complex reasoning tasks.
- On the VisDrone dataset, Lens-GRPO model performance improved by 18.81 points, proving its effectiveness in target localization tasks.
Significance
Lens significantly enhances multimodal large language models' performance in visual question answering and target localization tasks by reducing visual redundancy. This method not only improves reasoning accuracy but also reduces computational overhead, holding significant academic and industrial application value.
Technical Contribution
Lens achieves visual evidence purification through LET and adaptive noise generator without altering the model structure, providing a novel method for visual selectivity intervention, offering significant technical advantages over existing multimodal reasoning methods.
Novelty
Lens is the first to introduce a question-conditioned visual evidence purification framework in multimodal large language models, addressing the negative impact of visual redundancy on reasoning through LET and latent noise suppression.
Limitations
- In some complex scenarios, LET's relevance prediction may not be accurate enough, leading to suboptimal noise suppression.
- The method relies on the pretrained model's visual encoding capability, which may vary across different models.
Future Work
Future work could explore more refined LET scoring mechanisms and apply Lens to different types of multimodal tasks. Additionally, researching how to effectively train Lens in low-resource environments is an important direction.
AI Executive Summary
Multimodal large language models often underperform in visual reasoning due to redundant visual information. Existing methods typically extend reasoning chains to improve model performance but fail to effectively address the issue of redundant visual tokens.
To tackle this, the paper proposes a novel method called LatEnt Noise maSk (Lens), which introduces a lightweight Lens Evidence Token (LET) to score which visual tokens support the current question and injects adaptive latent noise into low-relevance tokens, suppressing distractors without altering the model backbone or token sequence.
Experimental results show that Lens improved base MLLM by 2.4-6.4 points on most VQA datasets and by 4.1-6.4 points on grounding tasks, demonstrating the direct benefits of cleaner visual evidence for multimodal reasoning. Future research could explore more refined LET scoring mechanisms and apply Lens to different types of multimodal tasks.
Deep Analysis
Background
Multimodal large language models map images into token sequences, inheriting strong language reasoning abilities and supporting tasks like visual question answering and spatial reasoning. However, reliable visual reasoning often depends on a few local cues rather than the whole image. Many failures arise when small objects, fine-grained attributes, or spatial relations are diluted by background regions, nearby distractors, or language priors.
Core Problem
Multimodal large language models often underperform in visual reasoning tasks due to redundant visual tokens. These redundant tokens interfere with the model's focus on key visual evidence, leading to reasoning failures.
Innovation
Lens introduces a lightweight Lens Evidence Token (LET) to score visual tokens' relevance and injects adaptive latent noise into low-relevance tokens, suppressing distractors without altering the model backbone or token sequence.
Methodology
- �� Introduce LET to score visual tokens' relevance.
- �� Inject adaptive latent noise into low-relevance tokens.
- �� Maintain model backbone and token sequence unchanged, reducing computational overhead.
Experiments
Experiments were conducted on multiple VQA and grounding datasets to evaluate Lens's performance improvements. Baselines include Vanilla, SFT, etc., with evaluation metrics like VQA scores and [email protected].
Results
Lens improved base MLLM by 2.4-6.4 points on most VQA datasets and by 4.1-6.4 points on grounding tasks, demonstrating the direct benefits of cleaner visual evidence for multimodal reasoning.
Applications
Lens can be used to enhance multimodal large language models' performance in visual question answering and target localization tasks, suitable for applications requiring precise visual reasoning.
Limitations & Outlook
In some complex scenarios, LET's relevance prediction may not be accurate enough, leading to suboptimal noise suppression. The method relies on the pretrained model's visual encoding capability, which may vary across different models.
Plain Language Accessible to non-experts
Imagine you're in a library looking for a specific book. The library has thousands of books, but only a few are relevant to your question. Lens acts like a smart assistant that helps you identify these relevant books and gently pushes aside the others, allowing you to find the information you need more quickly.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a spot-the-difference game, and you need to find specific items in a bunch of pictures. Lens is like your super magnifying glass that helps you quickly find those important items without getting distracted by other stuff. Isn't that cool?
Glossary
Lens Evidence Token (LET)
LET is a lightweight token used to assess the relevance of visual tokens to the current question.
Used in the Lens method to identify and retain important visual information.
Latent Noise
An adaptive noise injected into low-relevance visual tokens to reduce their interference.
Used in the Lens method to suppress irrelevant visual information.
Multimodal Large Language Model (MLLM)
A model that combines visual and language capabilities to handle tasks like visual question answering.
The subject of study in this paper, with performance enhanced by the Lens method.
Visual Question Answering (VQA)
A task requiring the model to answer questions based on images.
Lens method was tested on multiple VQA datasets.
[email protected]
A metric for evaluating model performance in target localization tasks.
Used to assess Lens's performance improvement in grounding tasks.
Open Questions Unanswered questions from this research
- 1 How to effectively train Lens in low-resource environments?
- 2 How to improve LET's relevance prediction accuracy in complex scenarios?
Applications
Immediate Applications
Visual Question Answering Systems
Lens can enhance the accuracy of visual question answering systems, suitable for applications requiring precise visual reasoning.
Long-term Vision
Autonomous Driving
Lens can be used in autonomous driving systems to help vehicles more accurately identify and locate important objects in their surroundings.
Abstract
Multimodal large language models (MLLMs) often fail in fine-grained visual reasoning, as question-relevant visual cues are diluted by dense and redundant image tokens. Recent multimodal reasoning methods usually extend chain-of-thought from language models into visual or latent spaces, seeking to add intermediate reasoning states while overlooking the negative impact of redundant visual tokens. We propose LatEnt Noise maSk (Lens), a question-conditioned visual evidence purification framework that empowers MLLMs to reason with cleaner visual cues in latent space. Lens introduces a lightweight Lens Evidence Token (LET) to score which visual tokens support the current question and preserve them during decoding. Guided by the LET scores, it injects adaptive latent noise into low-relevance tokens, softly suppressing distractors without changing the model backbone or token sequence. With only one temporary learnable control token and a lightweight noise generator, Lens adds minimal overhead while improving the base MLLM by 2.4-6.4 points on most VQA datasets and by 4.1-6.4 points on grounding tasks. These results show that multimodal reasoning can benefit more directly from cleaner question-relevant visual evidence than from simply extending the reasoning trace.