Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspective

2026-03-03

AI Summary

This work addresses catastrophic forgetting in incremental unified multi-modal anomaly detection, which is primarily caused by redundant and spurious features. To this end, the authors propose the IB-IUMAD framework, which, for the first time, elucidates the underlying mechanism from an information bottleneck perspective. The method employs a Mamba decoder to decouple feature entanglements among objects and introduces an information bottleneck fusion module that explicitly preserves discriminative information while filtering out redundancy. This enables both cross-category generalization and continual learning of novel classes. Experimental results on the MVTec 3D-AD and Eyecandies datasets demonstrate that the proposed approach effectively mitigates catastrophic forgetting and achieves competitive anomaly detection performance.

Technology Category

• Machine Learning: Multimodal Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The quest for incremental unified multimodal anomaly detection seeks to empower a single model with the ability to systematically detect anomalies across all categories and support incremental learning to accommodate emerging objects/categories. Central to this pursuit is resolving the catastrophic forgetting dilemma, which involves acquiring new knowledge while preserving prior learned knowledge. Despite some efforts to address this dilemma, a key oversight persists: ignoring the potential impact of spurious and redundant features on catastrophic forgetting. In this paper, we delve into the negative effect of spurious and redundant features on this dilemma in incremental unified frameworks, and reveal that under similar conditions, the multimodal framework developed by naive aggregation of unimodal architectures is more prone to forgetting. To address this issue, we introduce a novel denoising framework called IB-IUMAD, which exploits the complementary benefits of the Mamba decoder and information bottleneck fusion module: the former dedicated to disentangle inter-object feature coupling, preventing spurious feature interference between objects; the latter serves to filter out redundant features from the fused features, thus explicitly preserving discriminative information. A series of theoretical analyses and experiments on MVTec 3D-AD and Eyecandies datasets demonstrates the effectiveness and competitive performance of IB-IUMAD.

Problem

• incremental learning
• multimodal anomaly detection
• catastrophic forgetting
• spurious features
• redundant features

Innovation

• Incremental Learning
• Multimodal Anomaly Detection
• Information Bottleneck
• Catastrophic Forgetting
• Mamba Decoder