Zero-Shot Learning via Latent Space Encoding.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Zero-shot learning (ZSL) relies on transferring knowledge from seen to unseen classes using semantic embeddings.
• Effective ZSL requires capturing shared semantic characteristics between visual and semantic modalities.

Purpose of the Study:

• To propose a novel encoder-decoder approach, Latent Space Encoding (LSE), for connecting semantic relations across different modalities in ZSL.
• To develop a method that implicitly learns a feature-aware latent space for cross-modal interaction without explicit projection functions.

Main Methods:

• Latent Space Encoding (LSE) models different modalities separately but optimizes them jointly.
• An encoder-decoder framework is used for each modality to learn a latent space by maximizing recoverability and predictability.
• Features from different modalities referring to the same concept are enforced to share latent codings.

Main Results:

• The proposed LSE approach effectively generalizes common semantic characteristics across modalities using latent representations.
• Experimental results on four benchmark datasets demonstrate the superiority of LSE on traditional ZSL, generalized ZSL, and zero-shot retrieval tasks.
• The LSE approach is easily extendable to multiple modalities.

Conclusions:

• Latent Space Encoding (LSE) offers a powerful and flexible framework for advancing zero-shot learning.
• The method's ability to implicitly learn cross-modal relationships and generalize semantic features provides significant improvements in ZSL tasks.