Enhancing Target-unspecific Tasks through a Features Matrix

2025-05-06

AI Summary

To address the problem of catastrophic forgetting of general semantic knowledge and degraded generalization in large vision-language models (VLMs) during prompt tuning—caused by overfitting to task-irrelevant objectives—this paper proposes Feature Matrix (FM) regularization. Our method introduces a plug-and-play, structured feature matrix that explicitly models and disentangles high-level general semantic representations from the model’s deep layers, preserving generic knowledge without modifying the backbone architecture. Technically, FM regularization integrates multi-layer feature extraction, cross-sample semantic alignment, and matrix-based knowledge distillation, and is seamlessly embedded into mainstream prompt-learning frameworks. Evaluated on multiple task-agnostic benchmarks, it achieves state-of-the-art performance while maintaining strong plug-and-play compatibility across diverse VLM architectures. Empirical results demonstrate significant mitigation of overfitting and substantial improvement in task-agnostic generalization capability.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Recent developments in prompt learning of large vision-language models have significantly improved performance in target-specific tasks. However, these prompt optimizing methods often struggle to tackle the target-unspecific or generalizable tasks effectively. It may be attributed to the fact that overfitting training causes the model to forget its general knowledge having strong promotion on target-unspecific tasks. To alleviate this issue, we propose a novel Features Matrix (FM) regularization approach designed to enhance these models on target-unspecific tasks. Our method extracts and leverages general knowledge, shaping a Features Matrix (FM). Specifically, the FM captures the semantics of diverse inputs from a deep and fine perspective, preserving essential general knowledge, which mitigates the risk of overfitting. Representative evaluations demonstrate that: 1) the FM is compatible with existing frameworks as a generic and flexible module, and 2) the FM significantly showcases its effectiveness in enhancing target-unspecific tasks, achieving state-of-the-art performance.

Problem

• Improving performance in target-unspecific vision-language tasks
• Preventing overfitting while preserving general knowledge
• Enhancing model generalizability with Features Matrix regularization

Innovation

• Features Matrix regularization for general knowledge retention
• Deep and fine semantic capture from diverse inputs
• Compatible generic module enhancing target-unspecific tasks