i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data

no code implementations • 21 May 2023 • ZiYi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant, Yuwei Fang, Chenguang Zhu, Dongdong Chen, Yao Qian, Mei Gao, Yi-Ling Chen, Robert Gmyr, Naoyuki Kanda, Noel Codella, Bin Xiao, Yu Shi, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang

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 encoder-only models which lack generative abilities.

i-Code: An Integrative and Composable Multimodal Learning Framework

no code implementations • 3 May 2022 • ZiYi Yang, Yuwei Fang, Chenguang Zhu, Reid Pryzant, Dongdong Chen, Yu Shi, Yichong Xu, Yao Qian, Mei Gao, Yi-Ling Chen, Liyang Lu, Yujia Xie, Robert Gmyr, Noel Codella, Naoyuki Kanda, Bin Xiao, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang

Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview.

Contrastive Learning Video Understanding

Efficient Self-supervised Vision Transformers for Representation Learning

1 code implementation • ICLR 2022 • Chunyuan Li, Jianwei Yang, Pengchuan Zhang, Mei Gao, Bin Xiao, Xiyang Dai, Lu Yuan, Jianfeng Gao

This paper investigates two techniques for developing efficient self-supervised vision transformers (EsViT) for visual representation learning.

Representation Learning Self-Supervised Image Classification

Large-Scale Intelligent Microservices

1 code implementation • 17 Sep 2020 • Mark Hamilton, Nick Gonsalves, Christina Lee, Anand Raman, Brendan Walsh, Siddhartha Prasad, Dalitso Banda, Lucy Zhang, Mei Gao, Lei Zhang, William T. Freeman

Deploying Machine Learning (ML) algorithms within databases is a challenge due to the varied computational footprints of modern ML algorithms and the myriad of database technologies each with its own restrictive syntax.

Anomaly Detection