Multimodal

【论文阅读】CentralNet: a Multilayer Approach for Multimodal Fusion
CentralNet相比于Concatenate的创新点 Concate的方法相当于在各自模态的特征分别独立抽取之后做融合，但是不干预特征抽取的过程。这显然会漏掉一些不同模态之间的相关性的信息，
论文CentralNetMultilayerFusionMultimodal
admin4月前
630
【文献阅读】A Comprehensive Review of Multimodal Large Language Models
一、回顾MLLMs 在语言、图像、视频和音频处理等多模态任务中表现出色。这些模型通过整合多模态信息来增强多模态任务的有效性。在自然语言处理（NLP）任务中，如文本生成和机
文献ReviewComprehensiveMultimodallanguage
admin6月前
700
Comprehensive Multimodal Segmentation in Medical Imaging
作者未提供代码
MultimodalComprehensiveSegmentationImagingMedical
admin6月前
600
Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Surve
Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends
MultimodalLARGEAbilitiesEXPLORINGReasoning
admin6月前
360
A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences
本文是LLM系列文章，针对《Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences》的翻译
MultimodalLARGEComprehensiveBenchmarklanguage
admin6月前
530
AGI之MFM：《Multimodal Foundation Models: From Specialists to General-Purpose Assistants多模态基础模型：从专家到通用助
AGI之MFM：《Multimodal Foundation Models: From Specialists to General-Purpose Assistants多模态基础模型：从专家到通
模型多模基础专家Multimodal
admin7月前
460

【论文阅读】CentralNet: a Multilayer Approach for Multimodal Fusion

【文献阅读】A Comprehensive Review of Multimodal Large Language Models

Comprehensive Multimodal Segmentation in Medical Imaging

Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Surve

A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences

AGI之MFM：《Multimodal Foundation Models: From Specialists to General-Purpose Assistants多模态基础模型：从专家到通用助