Emotion recognition is the task of classifying perceived emotions in people.
Previous works have utilized various nonverbal cues to extract features from
images and correlate them to emotions. Of these cues, situational context is
particularly crucial in emotion perception since it can directly influence the
emotion of a person. In this paper, we propose an approach for high-level
context representation extraction from images. The model relies on a single cue
and a single encoding stream to correlate this representation with emotions.
Our model competes with the state-of-the-art, achieving an mAP of 0.3002 on the
EMOTIC dataset while also being capable of execution on consumer-grade hardware
at approximately 90 frames per second. Overall, our approach is more efficient
than previous models and can be easily deployed to address real-world problems
related to emotion recognition.

本文提出了一种从图像中提取高级语境表示的方法，利用单个线索和单个编码流与情感相关联，并且该模型的执行效率比以前的模型更高，其在 EMOTIC 数据集上达到了 0.3002 的 mAP 并且能够在消费级硬件上以每秒大约 90 帧的速度执行，容易部署在解决与情感识别相关的现实问题中。