Research map / Vision-language and multimodal / Human and embodied modeling
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Fine-tune ImageNet-CNN on strongly labeled emotion images | ImageNet-CNN · AMT-labeled test set | accuracy: 32.1% -> 58.3% | Helped |
| Use weak labels instead of strong AMT labels for emotion fine-tuning (ablation) | Fine-tuned-CNN · AMT-labeled test set | accuracy: 58.3% -> 45.8% | Hurt |
| Fine-tune on 156,000 weakly labeled images rather than the strongly labeled set (ablation) | ImageNet-CNN · AMT-labeled test set | accuracy: 58.3% -> 45.8% | Hurt |
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