论文标题
通过对抗性自动编码器从手势中学习看不见的情绪
Learning Unseen Emotions from Gestures via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders
论文作者
论文摘要
我们提出了一种新颖的广义零拍算法，以识别手势中感知的情绪。我们的任务是将手势映射到训练中未遇到的新型情感类别。我们介绍了一种基于对抗性的自动编码器的表示，该学习将3D运动捕获的手势序列与使用Word2Vec嵌入的自然语言感知情感术语的矢量表示相关。语言语义嵌入提供了情感标签空间的表示，我们利用这种基本的分布将手势序列映射到适当的分类情感标签上。我们使用注释的手势和已知情感术语和手势没有任何情感注释的手势的组合来训练我们的方法。我们在MPI情绪表达数据库（EBED）上评估我们的方法，并获得58.43美元的准确性。这将通用零局学习的当前最新算法的性能提高了25美元 - $ 27 \％\％$ $。
We present a novel generalized zero-shot algorithm to recognize perceived emotions from gestures. Our task is to map gestures to novel emotion categories not encountered in training. We introduce an adversarial, autoencoder-based representation learning that correlates 3D motion-captured gesture sequence with the vectorized representation of the natural-language perceived emotion terms using word2vec embeddings. The language-semantic embedding provides a representation of the emotion label space, and we leverage this underlying distribution to map the gesture-sequences to the appropriate categorical emotion labels. We train our method using a combination of gestures annotated with known emotion terms and gestures not annotated with any emotions. We evaluate our method on the MPI Emotional Body Expressions Database (EBEDB) and obtain an accuracy of $58.43\%$. This improves the performance of current state-of-the-art algorithms for generalized zero-shot learning by $25$--$27\%$ on the absolute.
