  Cross-modal retrieval (CMR) has been extensively applied in various domains,
such as multimedia search engines and recommendation systems. Most existing CMR
methods focus on image-to-text retrieval, whereas audio-to-text retrieval, a
less explored domain, has posed a great challenge due to the difficulty to
uncover discriminative features from audio clips and texts. Existing studies
are restricted in the following two ways: 1) Most researchers utilize
contrastive learning to construct a common subspace where similarities among
data can be measured. However, they considers only cross-modal transformation,
neglecting the intra-modal separability. Besides, the temperature parameter is
not adaptively adjusted along with semantic guidance, which degrades the
performance. 2) These methods do not take latent representation reconstruction
into account, which is essential for semantic alignment. This paper introduces
a novel audio-text oriented CMR approach, termed Contrastive Latent Space
Reconstruction Learning (CLSR). CLSR improves contrastive representation
learning by taking intra-modal separability into account and adopting an
adaptive temperature control strategy. Moreover, the latent representation
reconstruction modules are embedded into the CMR framework, which improves
modal interaction. Experiments in comparison with some state-of-the-art methods
on two audio-text datasets have validated the superiority of CLSR.
