most citedOmniBind: Large-scale Omni Multimodal Representation via Binding Spaces

1 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024★ 1 cited

OmniBind: Large-scale Omni Multimodal Representation via Binding Spaces

Zehan Wang, Ziang Zhang, Hang Zhang +5

Recently, human-computer interaction with various modalities has shown promising applications, like GPT-4o and Gemini. Given the foundational role of multimodal joint representatio…

cs.CV2024★ 1 cited

Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers

Peng Gao, Le Zhuo, Dongyang Liu +17

Sora unveils the potential of scaling Diffusion Transformer for generating photorealistic images and videos at arbitrary resolutions, aspect ratios, and durations, yet it still lac…

cs.CV2024

Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT

Le Zhuo, Ruoyi Du, Han Xiao +19

Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers that establishes a unified framework for transforming noise into various modalities, such as images and vi…

cs.CV2024

FreeBind: Free Lunch in Unified Multimodal Space via Knowledge Fusion

Zehan Wang, Ziang Zhang, Xize Cheng +8

Unified multi-model representation spaces are the foundation of multimodal understanding and generation. However, the billions of model parameters and catastrophic forgetting probl…

cs.CV2022★ 1 cited

Diffusion Denoising Process for Perceptron Bias in Out-of-distribution Detection

Luping Liu, Yi Ren, Xize Cheng +3

Out-of-distribution (OOD) detection is a crucial task for ensuring the reliability and safety of deep learning. Currently, discriminator models outperform other methods in this reg…