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meituan-longcat/LongCat-Flash-Omni
Any-to-Any • 561B • Updated • 189 • 100 -
LongCat-Flash-Omni Technical Report
Paper • 2511.00279 • Published • 22 -
OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM
Paper • 2510.15870 • Published • 89 -
nvidia/omnivinci
Feature Extraction • Updated • 6.83k • 163
Collections
Discover the best community collections!
Collections including paper arxiv:2511.00279
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Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis
Paper • 2412.15322 • Published • 20 -
Voila: Voice-Language Foundation Models for Real-Time Autonomous Interaction and Voice Role-Play
Paper • 2505.02707 • Published • 85 -
LLaMA-Omni2: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech Synthesis
Paper • 2505.02625 • Published • 22 -
Fast Text-to-Audio Generation with Adversarial Post-Training
Paper • 2505.08175 • Published • 25
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 29 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 14 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 44 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 23
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Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Paper • 2508.09789 • Published • 5 -
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
Paper • 2508.13186 • Published • 18 -
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
Paper • 2508.04038 • Published • 1 -
Prompt Orchestration Markup Language
Paper • 2508.13948 • Published • 48
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iVideoGPT: Interactive VideoGPTs are Scalable World Models
Paper • 2405.15223 • Published • 17 -
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models
Paper • 2405.15574 • Published • 55 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 90 -
Matryoshka Multimodal Models
Paper • 2405.17430 • Published • 34
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meituan-longcat/LongCat-Flash-Omni
Any-to-Any • 561B • Updated • 189 • 100 -
LongCat-Flash-Omni Technical Report
Paper • 2511.00279 • Published • 22 -
OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM
Paper • 2510.15870 • Published • 89 -
nvidia/omnivinci
Feature Extraction • Updated • 6.83k • 163
-
Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Paper • 2508.09789 • Published • 5 -
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
Paper • 2508.13186 • Published • 18 -
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
Paper • 2508.04038 • Published • 1 -
Prompt Orchestration Markup Language
Paper • 2508.13948 • Published • 48
-
Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis
Paper • 2412.15322 • Published • 20 -
Voila: Voice-Language Foundation Models for Real-Time Autonomous Interaction and Voice Role-Play
Paper • 2505.02707 • Published • 85 -
LLaMA-Omni2: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech Synthesis
Paper • 2505.02625 • Published • 22 -
Fast Text-to-Audio Generation with Adversarial Post-Training
Paper • 2505.08175 • Published • 25
-
iVideoGPT: Interactive VideoGPTs are Scalable World Models
Paper • 2405.15223 • Published • 17 -
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models
Paper • 2405.15574 • Published • 55 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 90 -
Matryoshka Multimodal Models
Paper • 2405.17430 • Published • 34
-
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 29 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 14 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 44 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 23