  Deep learning models have the ability to extract rich knowledge from
large-scale datasets. However, the sharing of data has become increasingly
challenging due to concerns regarding data copyright and privacy. Consequently,
this hampers the effective transfer of knowledge from existing data to novel
downstream tasks and concepts. Zero-shot learning (ZSL) approaches aim to
recognize new classes by transferring semantic knowledge learned from base
classes. However, traditional generative ZSL methods often require access to
real images from base classes and rely on manually annotated attributes, which
presents challenges in terms of data restrictions and model scalability. To
this end, this paper tackles a challenging and practical problem dubbed as
data-free zero-shot learning (DFZSL), where only the CLIP-based base classes
data pre-trained classifier is available for zero-shot classification.
Specifically, we propose a generic framework for DFZSL, which consists of three
main components. Firstly, to recover the virtual features of the base data, we
model the CLIP features of base class images as samples from a von Mises-Fisher
(vMF) distribution based on the pre-trained classifier. Secondly, we leverage
the text features of CLIP as low-cost semantic information and propose a
feature-language prompt tuning (FLPT) method to further align the virtual image
features and textual features. Thirdly, we train a conditional generative model
using the well-aligned virtual image features and corresponding semantic text
features, enabling the generation of new classes features and achieve better
zero-shot generalization. Our framework has been evaluated on five commonly
used benchmarks for generalized ZSL, as well as 11 benchmarks for the
base-to-new ZSL. The results demonstrate the superiority and effectiveness of
our approach. Our code is available in https://github.com/ylong4/DFZSL
