Loose Social-Interaction Recognition in Real-world Therapy Scenarios
| Type of publication: | Conference paper |
| Citation: | Ali_WACV_2025 |
| Publication status: | Accepted |
| Booktitle: | IEEE/CVF Winter Conference on Applications of Computer Vision |
| Year: | 2025 |
| Month: | February |
| Abstract: | The computer vision community has explored dyadic interactions for atomic actions such as pushing, carrying object, etc. However, with the advancement in deep learning models, there is a need to explore more complex dyadic situations such as loose interactions. These are interactions where two people perform certain atomic activities to complete a global action irrespective of temporal synchronisation and physical engagement, like cooking-together for example. Analysing these types of dyadic-interactions has several useful applications in the medical domain for social-skills development and mental health diagnosis. To achieve this, we propose a novel dual-path architecture to capture the loose interaction between two individuals. Our model learns global abstract features from each stream via a CNNs backbone and fuses them using a new Global-Layer-Attention module based on a cross-attention strategy. We evaluate our model on real-world autism diagnoses such as our Loose-Interaction dataset, and the publicly available Autism dataset for loose interactions. Our network achieves baseline results on the Loose-Interaction and SOTA results on the Autism datasets. Moreover, we study different social interactions by experimenting on a publicly available dataset i.e. NTU-RGB+D (interactive classes from both NTU-60 and NTU-120). We have found that different interactions require different network designs. We also compare a slightly different version of our method (details in Section 3.6) by incorporating time information to address tight interactions achieving SOTA results. |
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| Projects |
Idiap AI4Autism |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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