Papers › Semantic Diversity Learning for Zero-Shot Multi-label Classification
Semantic Diversity Learning for Zero-Shot Multi-label Classification
Avi Ben-Cohen, Nadav Zamir, Emanuel Ben Baruch, Itamar Friedman, Lihi Zelnik-Manor
Training a neural network model for recognizing multiple labels associated with an image, including identifying unseen labels, is challenging, especially for images that portray numerous semantically diverse labels. As challenging as this task is, it is an essential task to tackle since it represents many real-world cases, such as image retrieval of natural images. We argue that using a single embedding vector to represent an image, as commonly practiced, is not sufficient to rank both relevant seen and unseen labels accurately. This study introduces an end-to-end model training for multi-label zero-shot learning that supports semantic diversity of the images and labels. We propose to use an embedding matrix having principal embedding vectors trained using a tailored loss function. In addition, during training, we suggest up-weighting in the loss function image samples presenting higher semantic diversity to encourage the diversity of the embedding matrix. Extensive experiments show that our proposed method improves the zero-shot model's quality in tag-based image retrieval achieving SoTA results on several common datasets (NUS-Wide, COCO, Open Images).
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multi-label zero-shot learning | NUS-WIDE | SDL | mAP | 25.9 | #7 of 10 | Archive leaderboard | report |
| Multi-label zero-shot learning | Open Images V4 | ZS-SDL | MAP | 62.9 | #3 of 8 | Archive leaderboard | report |
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