52 citations · 139 across the 17 of their papers we have counts for
5 papers · 1 filter
CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation
Zineng Tang, Ziyi Yang, Mahmoud Khademi +3
We present CoDi-2, a versatile and interactive Multimodal Large Language Model (MLLM) that can follow complex multimodal interleaved instructions, conduct in-context learning (ICL)…
Soft Convex Quantization: Revisiting Vector Quantization with Convex Optimization
Tanmay Gautam, Reid Pryzant, Ziyi Yang +2
Vector Quantization (VQ) is a well-known technique in deep learning for extracting informative discrete latent representations. VQ-embedded models have shown impressive results in…
i-Code Studio: A Configurable and Composable Framework for Integrative AI
Yuwei Fang, Mahmoud Khademi, Chenguang Zhu +8
Artificial General Intelligence (AGI) requires comprehensive understanding and generation capabilities for a variety of tasks spanning different modalities and functionalities. Int…
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data
Ziyi Yang, Mahmoud Khademi, Yichong Xu +16
The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encod…
Any-to-Any Generation via Composable Diffusion
Zineng Tang, Ziyi Yang, Chenguang Zhu +2
We present Composable Diffusion (CoDi), a novel generative model capable of generating any combination of output modalities, such as language, image, video, or audio, from any comb…