MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification
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
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- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset” ≈ “Tongyi-MAI/Z-Image-Turbo””
- LinkedLinked via arxiv author · 85%Daniel Perkins →
“MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification”
- LinkedLinked via arxiv author · 85%John Squires →
“MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification”
- LinkedLinked via arxiv author · 85%Janou Milligan →
“MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification”
- LinkedLinked via arxiv author · 85%Chandra Raskoti →
“MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification”
- LinkedLinked via arxiv author · 85%Linda Ungerboeck →
“MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification”