Xi Chen, Xiao Wang et al
arXiv:2209.06794 (2022) paper link Google AI blog
PaLI (Pathways Language and Image model) achieves state-of-the-art in multiple vision and language tasks (such as captioning, visual question-answering, scene-text understanding), while retaining a simple, modular, and scalable design.
Jihyung Kil, Soravit Changpinyo, Xi Chen, Hexiang Hu, Sebastian Goodman, Wei-Lun Chao, Radu Soricut
arXiv:2209.05534 (2022) paper link
We propose PreSTU, a simple pre-training recipe specifically designed for scene-text understanding.
Soravit Changpinyo, Linting Xue, Idan Szpektor, Ashish V. Thapliyal, Julien Amelot, Xi Chen, Radu Soricut
arXiv:2209.05401 (2022) paper link
We propose scalable solutions to multi-lingual visual question answering (mVQA), on both data and modeling fronts. We first propose a translation-based framework to mVQA data generation that requires much less human annotation efforts than the conventional approach of directly collection questions and answers.
Ashish V. Thapliyal, Jordi Pont-Tuset, Xi Chen, Radu Soricut
arXiv:2205.12522 (2022) paper link
We present the Crossmodal-3600 dataset (XM3600 in short), a geographically diverse set of 3600 images annotated with human-generated reference captions in 36 languages.
Soravit Changpinyo, Doron Kukliansky, Idan Szpektor, Xi Chen, Nan Ding, Radu Soricut
arXiv:2205.01883 (2022) paper link Google AI blog
We propose a method that automatically derives VQA examples at volume, by leveraging the abundance of existing image-caption annotations combined with neural models for textual question generation.
Nan Ding, Xi Chen, Tomer Levinboim, Beer Changpinyo, Radu Soricut
arXiv:2203.05126 (2022) paper link
We present PACTran, a theoretically grounded family of metrics for pretrained model selection and transferability measurement.
Nan Ding, Xi Chen, Tomer Levinboim, Sebastian Goodman, Radu Soricut
NeurIPS 2021 paper link
We develop two PAC-Bayesian bounds tailored for the few-shot learning setting and show that two existing meta-learning algorithms (MAML and Reptile) can be derived from our bounds, thereby bridging the gap between practice and PAC-Bayesian theories.