Large-scale human behavioral data is needed to accelerate human-centered AI research and provide benchmarks for the field. To address this need, Hume AI is working with researchers and organizations around the world to co-organize machine learning competitions using our novel datasets. Our upcoming competitions center on understanding emotional expression using multi-modal, multi-task, generative, and few-shot learning methods.
Each competition will center on a new dataset capturing an understudied modality or context for human emotional behavior (e.g. voice, face, gesture, multi-modal, self-reported experience, social interaction).
We welcome both academic and industry teams to participate.
- (Completed) See ExVo2022 for the baseline code provided for the 2022 Expressive Vocalisations Competition held at ICML. The white paper describing those approaches can be found on arXiv.
- (Completed) See A-VB for the baseline code provided for the 2022 Affective Vocal Bursts Competition held at ACII. The white paper describing those approaches can be found on arXiv.
More information about our ML competitions can be found on our competitions webpage.