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Poster
Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone
Zi-Yi Dou · Aishwarya Kamath · Zhe Gan · Pengchuan Zhang · Jianfeng Wang · Linjie Li · Zicheng Liu · Ce Liu · Yann LeCun · Nanyun Peng · Jianfeng Gao · Lijuan Wang

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #635
in Poster Session 6 »

Vision-language (VL) pre-training has recently received considerable attention. However, most existing end-to-end pre-training approaches either only aim to tackle VL tasks such as image-text retrieval, visual question answering (VQA) and image captioning that test high-level understanding of images, or only target region-level understanding for tasks such as phrase grounding and object detection. We present FIBER (Fusion-In-the-Backbone-based transformER), a new VL model architecture that can seamlessly handle both these types of tasks. Instead of having dedicated transformer layers for fusion after the uni-modal backbones, FIBER pushes multimodal fusion deep into the model by inserting cross-attention into the image and text backbones to better capture multimodal interactions. In addition, unlike previous work that is either only pre-trained on image-text data or on fine-grained data with box-level annotations, we present a two-stage pre-training strategy that uses both these kinds of data efficiently: (i) coarse-grained pre-training based on image-text data; followed by (ii) fine-grained pre-training based on image-text-box data. We conduct comprehensive experiments on a wide range of VL tasks, ranging from VQA, image captioning, and retrieval, to phrase grounding, referring expression comprehension, and object detection. Using deep multimodal fusion coupled with the two-stage pre-training, FIBER provides consistent performance improvements over strong baselines across all tasks, often outperforming methods using magnitudes more data. Code is released at https://github.com/microsoft/FIBER.

Keywords: Object Detection · vision-language pre-training · VQA · image captioning
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Zi-Yi Dou (UCLA)
Aishwarya Kamath (New York University; DeepMind)
Zhe Gan (Microsoft)
Pengchuan Zhang (California Institute of Technology)
Jianfeng Wang (University of Science and Technology of China)
Linjie Li (Microsoft)
Zicheng Liu (Microsoft)
Ce Liu (Microsoft)
Yann LeCun (Facebook)

Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Data Science, Computer Science, Neural Science, and Electrical Engineering at New York University. He received the Electrical Engineer Diploma from ESIEE, Paris in 1983, and a PhD in Computer Science from Université Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he directed NYU's initiative in data science and became the founding director of the NYU Center for Data Science. He was named Director of AI Research at Facebook in late 2013 and retains a part-time position on the NYU faculty. His current interests include AI, machine learning, computer perception, mobile robotics, and computational neuroscience. He has published over 180 technical papers and book chapters on these topics as well as on neural networks, handwriting recognition, image processing and compression, and on dedicated circuits for computer perception.

Nanyun Peng (University of California, Los Angeles)
Jianfeng Gao (Microsoft Research, Redmond, WA)
Lijuan Wang

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