The challenge of learning a new concept, object, or a new medical disease recognition
without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the …

Zero-shot learning strives to classify unseen categories for which no data is available during
training. In the generalized variant, the test samples can further belong to seen or unseen …

We address the problem of fine-grained generalized zero-shot recognition of visually similar
classes without training images for some classes. We propose a dense attribute-based …

Most of the existing Zero-Shot Learning (ZSL) approaches learn direct embeddings from
global features or image parts (regions) to the semantic space, which, however, fail to …

Existing semantic segmentation models heavily rely on dense pixel-wise annotations. To
reduce the annotation pressure, we focus on a challenging task named zero-shot semantic …

Zero-shot learning (ZSL) is a challenging task due to the lack of unseen class data during
training. Existing works attempt to establish a mapping between the visual and class spaces …

Current deep visual recognition systems suffer from severe performance degradation when
they encounter new images from classes and scenarios unseen during training. Hence, the …

Zero-shot learning has been a tough problem since no labeled data is available for unseen
classes during training, especially for classes with low similarity. In this situation, transferring …

We develop a novel generative model for zero-shot learning to recognize fine-grained
unseen classes without training samples. Our observation is that generating holistic features …

Transfer learning between different language pairs has shown its effectiveness for Neural
Machine Translation (NMT) in low-resource scenario. However, existing transfer methods …