Vision and Language Pre-Trained Models

SOHO (“See Out of tHe bOx”) that takes a whole image as input, and learns vision-language representation in an end-to-end manner. SOHO does not require bounding box annotations which enables inference 10 times faster than region-based approaches. Text embeddings are used to extract textual embedding features. A trainable CNN is used to extract visual representations. SOHO learns to extract comprehensive yet compact image features through a visual dictionary (VD) that facilitates cross-modal understanding. VD is designed to represent consistent visual abstractions of similar semantics. It is updated on-the-fly and utilized in the proposed pre-training task Masked Visual Modeling (MVM).

Source: Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning


Paper Code Results Date Stars


Task Papers Share
BIG-bench Machine Learning 1 25.00%
Retrieval 1 25.00%
Visual Entailment 1 25.00%
Visual Reasoning 1 25.00%


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign