Affective Understanding
in Video

Videos allow us to capture and model the temporal nature of expressed affect, which is crucial in achieving human-level video understanding. Affective signals in videos are expressed over time across different modalities through music, scenery, camera angles, and movement, as well as with character tone, facial expressions, and body language. With the widespread availability of video recording technology and increasing storage capacity, we now have an expanding amount of public academic data to better study affect. Additionally, there is a growing number of temporal modeling methods that have not yet been well-explored for affective understanding in video.

Our workshop seeks to further explore this area and support researchers to compare models quantitatively at a large scale to improve the confidence, quality, and generalizability of the models. We encourage advances in datasets, models, and statistical techniques that leverage videos to improve our understanding of expressed affect and applications of these models to fields such as social assistive robotics, video retrieval and creation, and assistive driving that have direct and clear benefits to humans.

Topics include, but are not limited to:

• Methods to recognize affective expressions evoked in viewers by videos, such as from music/audio, scenes, character interactions, and related topics.

• Methods to recognize affective expressions of people shown in videos, including facial expression, body expressions, voice expression, and related topics.

• Ethics, bias and fairness in modeling and datasets for the problem of affective understanding in videos.

• Multimodal techniques for understanding affective expressions, including non-visual signals such as audio or speech.

• Explainability and interpretability in the context of affective video understanding.

• Temporal context and scene context in affective video understanding.

• Cross-cultural analysis of affect and subjective annotations.

• Open public academic datasets to understand affective expressions in video.

• Applications of affective understanding of videos to industry.