Multimodal Vision Transformers with Forced Attention for Behavior Analysis
This work addresses the problem of multimodal behavior analysis for designing more human-like machines, representing an incremental improvement over existing transformer methods.
The paper tackles the challenge of understanding human behavior in videos by introducing the Forced Attention (FAt) Transformer, which achieves state-of-the-art results on personality and body language recognition datasets like Udiva v0.5, First Impressions v2, and MPII Group Interaction.
Human behavior understanding requires looking at minute details in the large context of a scene containing multiple input modalities. It is necessary as it allows the design of more human-like machines. While transformer approaches have shown great improvements, they face multiple challenges such as lack of data or background noise. To tackle these, we introduce the Forced Attention (FAt) Transformer which utilize forced attention with a modified backbone for input encoding and a use of additional inputs. In addition to improving the performance on different tasks and inputs, the modification requires less time and memory resources. We provide a model for a generalised feature extraction for tasks concerning social signals and behavior analysis. Our focus is on understanding behavior in videos where people are interacting with each other or talking into the camera which simulates the first person point of view in social interaction. FAt Transformers are applied to two downstream tasks: personality recognition and body language recognition. We achieve state-of-the-art results for Udiva v0.5, First Impressions v2 and MPII Group Interaction datasets. We further provide an extensive ablation study of the proposed architecture.