PaLI-X: On Scaling up a Multilingual Vision and Language Model
Xi Chen Josip Djolonga Piotr Padlewski Basil Mustafa Soravit Changpinyo Jialin Wu Carlos Riquelme Ruiz Sebastian Goodman Xiao Wang Yi Tay Siamak Shakeri Mostafa Dehghani Daniel Salz Mario Lucic Michael Tschannen Arsha Nagrani Hexiang Hu Mandar Joshi Bo Pang Ceslee Montgomery Paulina Pietrzyk Marvin Ritter AJ Piergiovanni Matthias Minderer Filip Pavetic Austin Waters Gang Li Ibrahim Alabdulmohsin Lucas Beyer Julien Amelot Kenton Lee Andreas Peter Steiner Yang Li Daniel Keysers Anurag Arnab Yuanzhong Xu Keran Rong Alexander Kolesnikov Mojtaba Seyedhosseini Anelia Angelova Xiaohua Zhai Neil Houlsby Radu Soricut
We present the training recipe and results of scaling up PaLI-X, a
multilingual vision and language model, both in terms of size of the components
and the breadth of its training task mixture. Our model achieves new levels of
performance on a wide-range of varied and complex tasks, including multiple
image-based captioning and question-answering tasks, image-based document
understanding and few-shot (in-context) learning, as well as object detection,
video question answering, and video captioning. PaLI-X advances the
state-of-the-art on most vision-and-language benchmarks considered (25+ of
them). Finally, we observe emerging capabilities, such as complex counting and
multilingual object detection, tasks that are not explicitly in the training
mix.