Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification
This work addresses the challenge of classifying unseen categories in zero-shot learning, offering an incremental improvement over state-of-the-art generative adversarial network approaches.
The paper tackles the problem of zero-shot learning by enforcing semantic consistency across all stages, including training, feature synthesis, and classification, using a feedback loop and discriminative features. It outperforms existing methods on six benchmarks for object and action classification.
Zero-shot learning strives to classify unseen categories for which no data is available during training. In the generalized variant, the test samples can further belong to seen or unseen categories. The state-of-the-art relies on Generative Adversarial Networks that synthesize unseen class features by leveraging class-specific semantic embeddings. During training, they generate semantically consistent features, but discard this constraint during feature synthesis and classification. We propose to enforce semantic consistency at all stages of (generalized) zero-shot learning: training, feature synthesis and classification. We first introduce a feedback loop, from a semantic embedding decoder, that iteratively refines the generated features during both the training and feature synthesis stages. The synthesized features together with their corresponding latent embeddings from the decoder are then transformed into discriminative features and utilized during classification to reduce ambiguities among categories. Experiments on (generalized) zero-shot object and action classification reveal the benefit of semantic consistency and iterative feedback, outperforming existing methods on six zero-shot learning benchmarks. Source code at https://github.com/akshitac8/tfvaegan.