Papers with AREL
No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling (P18-1)
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| Challenge: | Visual captioning is aimed at depicting the concrete content of images, but its capability of performing human-like understanding is still restrictive. |
| Approach: | They propose an Adversarial REward Learning framework to learn an implicit reward function from human demonstrations and optimize policy search with the learned reward function. |
| Outcome: | The proposed framework improves performance over state-of-the-art (SOTA) methods in cloning expert behaviors, but human evaluation shows that it achieves significant improvement in generating more human-like stories than SOTA systems. |