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approfondimento - Multimodal Learning Framework
DREAMLLM: Synergistic Multimodal Comprehension and Creation at ICLR 2024
DREAMLLM is a versatile learning framework that enables the comprehension and creation of multimodal content through synergistic modeling.
Octavius: Mitigating Task Interference in Multimodal Large Language Models via LoRA-MoE
提案されたOctaviusフレームワークは、LoRA-MoEを活用して、複雑な学習シナリオにおけるタスク干渉の重要な課題に効果的に対処します。
DREAMLLM: Synergistic Multimodal Comprehension and Creation at ICLR 2024
DREAMLLM introduces a learning framework that enhances multimodal comprehension and creation, showcasing superior performance in zero-shot tasks.
PathM3: Multimodal Multi-Task Learning for Histopathology Image Analysis
PathM3 introduces a multimodal, multi-task, multiple instance learning framework for whole slide image classification and captioning in histopathology.
Octavius: Mitigating Task Interference in Multimodal Large Language Models via LoRA-MoE
Addressing task interference in multimodal learning with a novel framework, Octavius, integrating LoRA-MoE.
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