The proposed Dual Dynamic Threshold Adjustment Strategy (DDTAS) framework can be extended to other deep learning tasks beyond image retrieval by adapting the concept of dynamic threshold adjustment to suit the specific requirements of tasks such as object detection or semantic segmentation.
For object detection, the DDTAS framework can be applied by incorporating dynamic threshold adjustment into the process of selecting region proposals or bounding boxes. The thresholds can be dynamically adjusted based on the characteristics of the objects being detected, allowing for more accurate and efficient detection.
In the case of semantic segmentation, the DDTAS framework can be utilized to adjust thresholds for pixel-wise classification. By dynamically adapting the thresholds based on the context of the image and the surrounding pixels, the segmentation process can be optimized to achieve better segmentation results.
Overall, the key to extending the DDTAS framework to other deep learning tasks lies in customizing the threshold adjustment process to align with the specific requirements and challenges of each task, thereby enhancing the performance and efficiency of the models in tasks beyond image retrieval.