Zero-Shot Visual Recognition using Semantics-Preserving
Adversarial Embedding Networks
Abstract
We propose a novel framework called SemanticsPreserving Adversarial Embedding Network (SP-AEN) for
zero-shot visual recognition (ZSL), where test images and
their classes are both unseen during training. SP-AEN
aims to tackle the inherent problem — semantic loss —
in the prevailing family of embedding-based ZSL, where
some semantics would be discarded during training if they
are non-discriminative for training classes, but could become critical for recognizing test classes. Specifically, SPAEN prevents the semantic loss by introducing an independent visual-to-semantic space embedder which disentangles the semantic space into two subspaces for the two arguably conflicting objectives: classification and reconstruction. Through adversarial learning of the two subspaces,
SP-AEN can transfer the semantics from the reconstructive
subspace to the discriminative one, accomplishing the improved zero-shot recognition of unseen classes. Comparing