This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples
under the condition that some output classes are unknown during supervised learning. To …

We aim at advancing open-vocabulary object detection, which detects objects described by
arbitrary text inputs. The fundamental challenge is the availability of training data. It is costly …

Zero-shot semantic segmentation (ZS3) aims to segment the novel categories that have not
been seen in the training. Existing works formulate ZS3 as a pixel-level zero-shot …

Learning visual representations from natural language supervision has recently shown great
promise in a number of pioneering works. In general, these language-augmented visual …

The goal of this work is to establish a scalable pipeline for expanding an object detector
towards novel/unseen categories, using zero manual annotations. To achieve that, we make …

Most machine-learning methods focus on classifying instances whose classes have already
been seen in training. In practice, many applications require classifying instances whose …

One of the most challenging question types in VQA is when answering the question requires
outside knowledge not present in the image. In this work we study open-domain knowledge …

Suffering from the extreme training data imbalance between seen and unseen classes, most
of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging …

Due to the importance of zero-shot learning, ie, classifying images where there is a lack of
labeled training data, the number of proposed approaches has recently increased steadily …