The paper introduces the MICL scenario for MLLMs with modality and task shifts and proposes MoInCL using pseudo-target generation and instruction-based distillation, reporting gains over continual learning baselines on six tasks.
LA VIS: A Library for Language-Vision Intelligence,
3 Pith papers cite this work, alongside 21 external citations. Polarity classification is still indexing.
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Pith papers citing it
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representative citing papers
Large vision-language models exhibit severe object hallucination that varies with training instructions, and the proposed POPE polling method evaluates it more stably and flexibly than prior approaches.
A survey synthesizing LLM and MM-LLM uses in transportation operations, mobility services, and decision support while noting challenges like data heterogeneity and real-time needs.
citing papers explorer
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Modality-Inconsistent Continual Learning of Multimodal Large Language Models
The paper introduces the MICL scenario for MLLMs with modality and task shifts and proposes MoInCL using pseudo-target generation and instruction-based distillation, reporting gains over continual learning baselines on six tasks.
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Evaluating Object Hallucination in Large Vision-Language Models
Large vision-language models exhibit severe object hallucination that varies with training instructions, and the proposed POPE polling method evaluates it more stably and flexibly than prior approaches.
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Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support
A survey synthesizing LLM and MM-LLM uses in transportation operations, mobility services, and decision support while noting challenges like data heterogeneity and real-time needs.