The accuracies for many pattern recognition tasks have increased rapidly year by year,
achieving or even outperforming human performance. From the perspective of accuracy …

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 …

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 …

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 …

In reinforcement learning for visual navigation, it is common to develop a model for each
new task, and train that model from scratch with task-specific interactions in 3D …

Visual attributes constitute a large portion of information contained in a scene. Objects can
be described using a wide variety of attributes which portray their visual appearance (color …

Zero-shot learning (ZSL) aims to accurately recognize unseen objects by learning mapping
matrices that bridge the gap between visual information and semantic attributes. Previous …

Human-annotated attributes serve as powerful semantic embeddings in zero-shot learning.
However, their annotation process is labor-intensive and needs expert supervision. Current …

Vision-language modeling has enabled open-vocabulary tasks where predictions can be
queried using any text prompt in a zero-shot manner. Existing open-vocabulary tasks focus …

Prevalent techniques in zero-shot learning do not generalize well to other related problem
scenarios. Here, we present a unified approach for conventional zero-shot, generalized zero …