MLLMルーティングによる異種アンサンブルを用いた頑健なクロスデータセット画像分類
MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification
マルチモーダル大規模言語モデル(MLLM)エージェントが各画像を最適な視覚バックボーンに動的ルーティングするアンサンブル手法ARMDILを提案し、複数データセットにまたがる画像分類の頑健性と適応性を向上させた。
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著者: Daniel Perkins, John Squires, Janou Milligan, Chandra Raskoti, Linda Ungerboeck
分類: cs.CV, cs.AI, cs.CL, cs.LG
原文アブストラクト
Modern image classification models excel when trained on single task-specific datasets but often struggle to generalize across domains and difficulty levels. We propose ARMDIL, an Adaptive Router for Multi-Domain Image classification with LLMs. ARMDIL is an ensemble that uses a multimodal large language model (MLLM) agent to dynamically route each image to the most suitable vision backbone. Our diverse ensemble employs convolutional neural networks (ResNets), self-supervised representation learners (SSL), and vision-language models (VLMs), each trained on a unified label space constructed from multiple image datasets with differing distributions and characteristics. Empirical evaluations illuminate the distinct capabilities and vulnerabilities of each architecture across disparate visual domains. Crucially, we show that ARMDIL effectively navigates these trade-offs, performing competitively with specialized training-based routers. Furthermore, it drastically improves adaptability by allowing new information to be integrated via simple prompt modifications, while enhancing interpretability through natural language reasoning traces. These advances in cross-dataset image classification pave the way for more reliable general-purpose vision systems such as AI assistants and autonomous robots.