Reconstruct before Query: Continual Missing Modality Learning with Decomposed Prompt Collaboration
RebQ framework improves average precision from 20.00 to 50.92 and reduces forgetting from 75.95 to 8.56 in missing modality learning.
Key Findings
Methodology
RebQ framework decomposes prompts into modality-specific components stored in pools accessed via a key-query mechanism, enabling parameter-efficient fine-tuning and enhancing knowledge transferability. It leverages pre-trained multi-modal models to reconstruct missing modality data.
Key Results
- RebQ framework on UPMC-Food101-CMML dataset improves average precision from 20.00 to 50.92 and reduces average forgetting from 75.95 to 8.56.
- On MM-IMDb-CMML dataset, RebQ significantly outperforms baseline methods, demonstrating its advantage in multi-label continual missing modality learning.
- RebQ significantly improves knowledge transferability by reconstructing missing queries.
Significance
This research provides a novel approach to address the missing modality problem, especially significant for privacy-sensitive and resource-constrained devices. It not only enhances model performance in missing modality scenarios but also reduces catastrophic forgetting.
Technical Contribution
RebQ framework offers a new solution by decomposing prompts and reconstructing missing queries, significantly improving knowledge transferability and model adaptability compared to existing methods.
Novelty
RebQ is the first to use decomposed prompts and query reconstruction in continual missing modality learning, overcoming limitations of traditional methods in dynamic environments.
Limitations
- RebQ may perform poorly in extreme missing modality scenarios, as reconstructed queries may not be accurate enough.
- Requires extensive pre-trained multi-modal knowledge, which can be challenging for resource-limited devices.
Future Work
Future work could explore applying RebQ in more complex multi-modal environments and further optimizing the query reconstruction process.
AI Executive Summary
In the field of multi-modal learning, existing methods face challenges in handling missing modalities, especially in continual learning. The RebQ framework provides a new solution by decomposing prompts and reconstructing missing queries. Experimental results show that RebQ significantly improves model performance in missing modality scenarios, particularly on UPMC-Food101-CMML and MM-IMDb-CMML datasets. This method not only reduces catastrophic forgetting but also enhances knowledge transferability. However, RebQ still has limitations in handling extreme missing modality scenarios, and future work could further optimize its query reconstruction process.
Deep Analysis
Background
Multi-modal learning has made significant progress recently, especially in integrating image and text data. However, with privacy concerns and technical limitations, sensors may be turned off, leading to missing modality data. Existing methods often face catastrophic forgetting when dealing with such dynamic environments.
Core Problem
The core problem in missing modality learning is how to effectively handle missing modality data during continual learning, especially when fine-tuning on resource-constrained devices. Traditional methods often suffer performance drops when dealing with dynamic environments, struggling to maintain knowledge integrity.
Innovation
RebQ framework decomposes prompts into modality-specific components stored in pools and accesses these pools via reconstructed missing queries. This method not only improves knowledge transferability but also reduces catastrophic forgetting.
Methodology
- �� Decompose prompts into modality-specific components
- �� Use key-query mechanism to access prompt pools
- �� Leverage pre-trained multi-modal knowledge to reconstruct missing queries
- �� Perform parameter-efficient fine-tuning in LMM
Experiments
Experiments use UPMC-Food101-CMML and MM-IMDb-CMML datasets with different missing modality ratios. Baseline methods include MAP, MSP, L2P, and DualPrompt. Evaluation metrics are average precision and average forgetting.
Results
RebQ significantly improves average precision and reduces forgetting on UPMC-Food101-CMML dataset. On MM-IMDb-CMML dataset, RebQ excels in multi-label tasks, demonstrating its advantage in handling complex multi-modal environments.
Applications
RebQ can be applied in privacy-sensitive surveillance systems and resource-constrained devices, helping these systems operate effectively even when modality data is missing.
Limitations & Outlook
RebQ may perform poorly in extreme missing modality scenarios and requires extensive pre-trained multi-modal knowledge, which can be challenging for resource-limited devices.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen and sometimes you lack certain ingredients. RebQ acts like a smart assistant that can infer and supplement missing parts based on available ingredients, allowing you to still make delicious dishes. Even if some ingredients are not on hand, it can use previous experience to help you complete the cooking.
ELI14 Explained like you're 14
Imagine you're playing a game and suddenly an important item is missing. RebQ acts like a super smart assistant that can help you find this item based on other clues in the game. Even if some clues are not obvious, it can use previous experience to help you continue the game.
Glossary
Multi-modal Model
A model capable of processing multiple types of data, such as images and text.
Used to handle scenarios with missing modality data.
Catastrophic Forgetting
When a model forgets previous knowledge while learning new tasks.
A common issue in continual learning.
Parameter-Efficient Fine-Tuning
Adapting to new tasks by adjusting a small number of parameters.
Used for fine-tuning on resource-constrained devices.
Prompt Learning
Guiding a model to learn new knowledge through prompts.
Used to enhance knowledge transferability.
Key-Query Mechanism
Accessing stored prompts through key-value pairs.
Used to access components in prompt pools.
Open Questions Unanswered questions from this research
- 1 How to further enhance RebQ's performance in extreme missing modality scenarios?
- 2 How to optimize RebQ's query reconstruction process on resource-constrained devices?
Applications
Immediate Applications
Privacy-sensitive Surveillance
Apply RebQ in privacy-sensitive surveillance systems to help them operate effectively even when modality data is missing.
Long-term Vision
Smart Device Optimization
Apply RebQ in resource-constrained smart devices to optimize their adaptability in dynamic environments.
Abstract
Pre-trained large multi-modal models (LMMs) exploit fine-tuning to adapt diverse user applications. Nevertheless, fine-tuning may face challenges due to deactivated sensors (e.g., cameras turned off for privacy or technical issues), yielding modality-incomplete data and leading to inconsistency in training data and the data for inference. Additionally, continuous training leads to catastrophic forgetting, diluting the knowledge in pre-trained LMMs. To overcome these challenges, we introduce a novel task, Continual Missing Modality Learning (CMML), to investigate how models can generalize when data of certain modalities is missing during continual fine-tuning. Our preliminary benchmarks reveal that existing methods suffer from a significant performance drop in CMML, even with the aid of advanced continual learning techniques. Therefore, we devise a framework termed Reconstruct before Query (RebQ). It decomposes prompts into modality-specific ones and breaks them into components stored in pools accessible via a key-query mechanism, which facilitates ParameterEfficient Fine-Tuning and enhances knowledge transferability for subsequent tasks. Meanwhile, our RebQ leverages extensive multi-modal knowledge from pre-trained LMMs to reconstruct the data of missing modality. Comprehensive experiments demonstrate that RebQ effectively reconstructs the missing modality information and retains pre-trained knowledge. Specifically, compared with the baseline, RebQ improves average precision from 20.00 to 50.92 and decreases average forgetting from 75.95 to 8.56. Code and datasets are available on https://github.com/Tree-Shu-Zhao/RebQ.pytorch