Multimodal large language models

Results 91 to 100 of about 16,504,576 (273)

EIM: An effective solution for improving multi-modal large language models

PLoS ONE
Enabling large language models (LLMs) to have multi-modal capabilities, such as vision-language learning, has become a current research hotspot and the next milestone in LLM development with the advent of models like GPT4.
Yuting Bai, Tonghua Su, Zixing Bai
doaj +2 more sources

Large multimodal models for low-resource languages: A survey

Information Fusion
Accepted in Information ...
Marian Lupascu +3 more
openaire +4 more sources

Multimodal large language models in human-centered health: practical insights

Multimodal data offer a comprehensive view of human health by integrating diverse sources, such as text, medical images, physiological signals, and behavioral data.
Marcu, Gabriela, Dang, Ting, Jia, Hong
core +1 more source

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley +1 more source

Woodpecker: hallucination correction for multimodal large language models

Science China Information Sciences
Hallucination is a big shadow hanging over the rapidly evolving Multimodal Large Language Models (MLLMs), referring to the phenomenon that the generated text is inconsistent with the image content. In order to mitigate hallucinations, existing studies mainly resort to an instruction-tuning manner that requires retraining the models with specific data ...
Shukang Yin +9 more
openaire +4 more sources

Auditory–Tactile Congruence for Synthesis of Adaptive Pain Expressions in RoboPatients

Advanced Robotics Research, EarlyView.
In this work, we explore auditory–tactile congruence for synthesizing adaptive vocal pain expressions in robopatients. Using a robopatient platform that integrates vocal pain sounds with palpation forces, we conducted 7680 trials across 20 participants.
Saitarun Nadipineni +4 more
wiley +1 more source

Language Models in Video Anomaly Detection: A Dynamic Survey

IEEE Access
Video Anomaly Detection (VAD) plays a critical role in intelligent surveillance and public safety by identifying rare, unexpected, or contextually abnormal events in complex video environments.
Furkan Mumcu +4 more
doaj +1 more source

Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models

, 2023
The burgeoning interest in Multimodal Large Language Models (MLLMs), such as OpenAI's GPT-4V(ision), has significantly impacted both academic and industrial realms.
Zhao, Yun, Wang, Yuqing
core

Multimodal Engagement Assessment in Children During Invented Story Paradigm With a Social Robot

Advanced Robotics Research, EarlyView.
A multimodal framework is proposed to assess children's engagement during storytelling interactions with a social robot. Gaze, physiological, and behavioral data are combined and validated against observer ratings. An automated gaze‐labeling strategy is introduced, and supervised classifiers achieve high accuracy. The study supports scalable engagement
Laura Fiorini +7 more
wiley +1 more source

Multimodal Large Language Models and Tunings: Vision, Language, Sensors, Audio, and Beyond [PDF]

This tutorial explores recent advancements in multimodal pretrained and large models, capable of integrating and processing diverse data forms such as text, images, audio, and video. Participants will gain an understanding of the foundational concepts of
Navigli, Roberto +3 more
core +1 more source

large language models
artificial intelligence
large language model