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Found 4 papers, 2 papers with code
Detect2Interact: Localizing Object Key Field in Visual Question Answering (VQA) with LLMs
As a result, Detect2Interact achieves consistent qualitative results on object key field detection across extensive test cases and outperforms the existing VQA system with object detection by providing a more reasonable and finer visual representation.
A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction
As a result, we propose a solution that explicitly takes both individual joint features and inter-joint features as input, relieving the system from the need of discovering more complicated features from small data.
A Skeleton-aware Graph Convolutional Network for Human-Object Interaction Detection
Our network exploits the spatial connections between human keypoints and object keypoints to capture their fine-grained structural interactions via graph convolutions.
Ranked #22 on
Human-Object Interaction Detection
on V-COCO
Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention
To highlight the capacity of the deep network in modelling input features, we utilize raw joint positions instead of hand-crafted features.
