To adapt JCRA's architecture and training for joint detection of human poses and bounding boxes, similar to Mask R-CNN, the following steps can be taken:
Integration of Object Detection Head: Modify the JCRA architecture to include an object detection head that can predict bounding boxes around individuals in the image. This additional head can be connected to the existing architecture to enable the joint detection of poses and bounding boxes.
Multi-Task Learning: Implement multi-task learning during training, where the model is trained to simultaneously optimize for both pose estimation and object detection tasks. This can be achieved by combining the loss functions of both tasks and updating the model parameters accordingly.
Bounding Box Regression: Introduce a bounding box regression module in the network that can predict the coordinates of bounding boxes around detected individuals. This module can be trained alongside the pose estimation components to learn the spatial relationships between poses and bounding boxes.
Fine-Tuning Training Data: Fine-tune the training data to include annotations for both human poses and bounding boxes. This annotated dataset can be used to train the model in a supervised manner to learn the joint detection task effectively.
Evaluation Metrics: Define appropriate evaluation metrics that consider the accuracy of both pose estimation and bounding box detection. This will ensure that the model's performance is assessed comprehensively for real-world applications.
By incorporating these adaptations, JCRA can be transformed into a comprehensive framework capable of joint detection of human poses and bounding boxes, enhancing its utility and effectiveness in various real-world scenarios.