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multimodal generation

Multimodal generation refers to the process of generating outputs that incorporate multiple modalities, such as images, text, and sound. This can be done using deep learning models that are trained on data that includes multiple modalities, allowing the models to generate output that is informed by more than one type of data.

For example, a multimodal generation model could be trained to generate captions for images that incorporate both text and visual information. The model could learn to identify objects in the image and generate descriptions of them in natural language, while also taking into account contextual information and the relationships between the objects in the image.

Multimodal generation can also be used in other applications, such as generating realistic images from textual descriptions or generating audio descriptions of video content. By combining multiple modalities in this way, multimodal generation models can produce more accurate and comprehensive output, making them useful for a wide range of applications.

Papers

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Showing 51–75 of 98 papers

TitleStatusHype
Multi-modal Latent DiffusionCode0
D-Judge: How Far Are We? Evaluating the Discrepancies Between AI-synthesized Images and Natural Images through Multimodal GuidanceCode0
PixelBytes: Catching Unified Embedding for Multimodal GenerationCode0
Continual and Multi-Task Architecture SearchCode0
MMMG: a Comprehensive and Reliable Evaluation Suite for Multitask Multimodal Generation—0
Unlocking Pretrained LLMs for Motion-Related Multimodal Generation: A Fine-Tuning Approach to Unify Diffusion and Next-Token Prediction—0
Benchmarking Multimodal Models for Ukrainian Language Understanding Across Academic and Cultural Domains—0
A Survey on Vision Autoregressive Model—0
Multimodal Pretraining and Generation for Recommendation: A Tutorial—0
Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models—0
Visatronic: A Multimodal Decoder-Only Model for Speech Synthesis—0
A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond—0
3D-VLA: A 3D Vision-Language-Action Generative World Model—0
A Survey of Emerging Approaches and Advances in Video Generation—0
Artificial Intelligence in Creative Industries: Advances Prior to 2025—0
ACDC: Autoregressive Coherent Multimodal Generation using Diffusion Correction—0
PCQA: A Strong Baseline for AIGC Quality Assessment Based on Prompt Condition—0
Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation—0
PlanMoGPT: Flow-Enhanced Progressive Planning for Text to Motion Synthesis—0
Preliminary Explorations with GPT-4o(mni) Native Image Generation—0
ProtTeX: Structure-In-Context Reasoning and Editing of Proteins with Large Language Models—0
RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction—0
ARMOR v0.1: Empowering Autoregressive Multimodal Understanding Model with Interleaved Multimodal Generation via Asymmetric Synergy—0
DiffuSIA: A Spiral Interaction Architecture for Encoder-Decoder Text Diffusion—0
Diffusion Models For Multi-Modal Generative Modeling—0
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