Papers › Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification
Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification
Sanath Narayan, Akshita Gupta, Fahad Shahbaz Khan, Cees G. M. Snoek, Ling Shao
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.
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Generalized Zero-Shot Learning | AwA2 | GZSL_TF-VAEGAN | Harmonic mean | 66.6 | #3 of 4 | Archive leaderboard | report |
| Generalized Zero-Shot Learning | CUB-200-2011 | GZSL_TF-VAEGAN | Harmonic mean | 58.1 | #3 of 3 | Archive leaderboard | report |
| Generalized Zero-Shot Learning | Oxford 102 Flower | GZSL_TF-VAEGAN | Harmonic mean | 71.7 | #2 of 2 | Archive leaderboard | report |
| Generalized Zero-Shot Learning | SUN Attribute | GZSL_TF-VAEGAN | Harmonic mean | 43 | #3 of 9 | Archive leaderboard | report |
| Zero-Shot Learning | AwA2 | ZSL_TF-VAEGAN | average top-1 classification accuracy | 72.2 | #3 of 4 | Archive leaderboard | report |
| Zero-Shot Learning | CUB-200-2011 | ZSL_TF-VAEGAN | average top-1 classification accuracy | 64.9 | #5 of 14 | Archive leaderboard | report |
| Zero-Shot Learning | Oxford 102 Flower | ZSL_TF-VAEGAN | average top-1 classification accuracy | 70.8 | #2 of 2 | Archive leaderboard | report |
| Zero-Shot Learning | SUN Attribute | ZSL_TF-VAEGAN | average top-1 classification accuracy | 66 | #3 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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