Recent works have shown that unstructured text (documents) from online sources can serve
as useful auxiliary information for zero-shot image classification. However, these methods …

Convolutional neural networks (CNNs) have demonstrated gratifying results at learning
discriminative features. However, when applied to unseen domains, state-of-the-art models …

Despite the tremendous progress in zero-shot learning (ZSL), the majority of existing
methods still rely on human-annotated attributes, which are difficult to annotate and scale …

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

Zero-shot learning (ZSL) aims to recognize instances of unseen classes solely based on the
semantic descriptions of the classes. Existing algorithms usually formulate it as a semantic …

In the evolution of agriculture to its next stage, Agriculture 5.0, artificial intelligence will play a
central role. Controlled-environment agriculture, or CEA, is a special form of urban and …

Zero-shot learning has been well explored to precisely identify new unobserved classes
through a visual-semantic function obtained from the existing objects. However, there exist …

Zero-shot learning (ZSL) typically suffers from the domain shift issue since the projected
feature embedding of unseen samples mismatch with the corresponding class semantic …

Using generative models to synthesize visual features from semantic distribution is one of
the most popular solutions to ZSL image classification in recent years. The triplet loss (TL) is …

The existing Zero-Shot learning (ZSL) methods may suffer from the vague class attributes
that are highly overlapped for different classes. Unlike these methods that ignore the …