S-Extension Patch: A simple and efficient way to extend an object detection model
This addresses the computational burden of retraining for practitioners needing to add capabilities to existing object detection systems.
The paper tackles the problem of efficiently extending object detection models to new classes without full retraining, achieving class extension in 1/10th the time of existing methods while maintaining inference speed and accuracy.
While building convolutional network-based systems, the toll it takes to train the network is something that cannot be ignored. In cases where we need to append additional capabilities to the existing model, the attention immediately goes towards retraining techniques. In this paper, I show how to leverage knowledge about the dataset to append the class faster while maintaining the speed of inference as well as the accuracies; while reducing the amount of time and data required. The method can extend a class in the existing object detection model in 1/10th of the time compared to the other existing methods. S-Extension patch not only offers faster training but also speed and ease of adaptation, as it can be appended to any existing system, given it fulfills the similarity threshold condition.