  We propose Pixel-BERT to align image pixels with text by deep multi-modal
transformers that jointly learn visual and language embedding in a unified
end-to-end framework. We aim to build a more accurate and thorough connection
between image pixels and language semantics directly from image and sentence
pairs instead of using region-based image features as the most recent vision
and language tasks. Our Pixel-BERT which aligns semantic connection in pixel
and text level solves the limitation of task-specific visual representation for
vision and language tasks. It also relieves the cost of bounding box
annotations and overcomes the unbalance between semantic labels in visual task
and language semantic. To provide a better representation for down-stream
tasks, we pre-train a universal end-to-end model with image and sentence pairs
from Visual Genome dataset and MS-COCO dataset. We propose to use a random
pixel sampling mechanism to enhance the robustness of visual representation and
to apply the Masked Language Model and Image-Text Matching as pre-training
tasks. Extensive experiments on downstream tasks with our pre-trained model
show that our approach makes the most state-of-the-arts in downstream tasks,
including Visual Question Answering (VQA), image-text retrieval, Natural
Language for Visual Reasoning for Real (NLVR). Particularly, we boost the
performance of a single model in VQA task by 2.17 points compared with SOTA
under fair comparison.
