Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic
interactions to transfer semantic knowledge from seen classes to unseen ones supported by …

With the goal to detect both the object categories appearing in the training phase and those
never have been observed before testing, zero-shot object detection (ZSD) becomes a …

Generative models have attracted much attention for handling the generalized zero-shot
learning (GZSL) task recently. Most of the existing generative GZSL models are trained for …

Generalized Zero-Shot Learning (GZSL) aims to recognize both seen and unseen
categories by establishing visual and semantic relations. Recently, generation-based …

Diffusion-based algorithms have emerged as promising techniques for weight generation,
particularly in scenarios like multi-task learning that require frequent weight updates …

Zero-shot image classification enables the recognition of new categories without requiring
additional training data, thereby enhancing the model's generalization capability when …

Although high resolution range profile (HRRP) target recognition methods have achieved
satisfactory performance in closed set recognition tasks, they are not able to recognize …

Zero-shot learning (ZSL) aims to recognize unseen classes by transferring semantic
knowledge from seen classes to unseen ones, guided by semantic information. To this end …

Our paper addresses the challenge of generalized zero-shot learning, where the label of a
target image may belong to either a seen or an unseen category. Previous methods for this …

This paper introduces a novel framework for zero-shot learning (ZSL), ie, to recognize new
categories that are unseen during training, by using a multi-model and multi-alignment …