• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.77-84
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• 2022

In this paper, we propose a Label Reassignment Strategy to improve the performance of an object detection algorithm. Our approach involves two stages: an inference stage and an assignment stage. In the inference stage, we perform multi-scale inference with predefined scale sizes on a trained model and re-infer masked images to obtain robust classification results. In the assignment stage, we calculate the IoU between bounding boxes to remove duplicates. We also check box and class occurrence between the detection result and annotation label to re-assign the dominant class type. We trained the YOLOX-L model with the re-annotated dataset to validate our strategy. The model achieved a 3.9% improvement in mAP and 3x better performance on AP_S compared to the model trained with the original dataset. Our results demonstrate that the proposed Label Reassignment Strategy can effectively improve the performance of an object detection model.

• Journal of Korea Multimedia Society
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• v.5 no.4
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• pp.372-385
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• 2002

In this paper, we propose an efficient buffer replacement method for the real-time multimedia data. The proposed method has multi level priority to consider the real-time characteristics. Each priority level is divided into a cold data set that is likely to be referenced for the first time and a hot data set that is likely to be re-referenced. An operation to select the victim data is sequentially executed from the cold set with the minimum priority level to the hot set with the maximum Priority level. It is chosen only at the lower level than or equal to the priority of the transaction that requests a buffer allocation. In the cold set, our method selects a media that has the maximum size in the level for a target of victim first of all. And in the hot set, our method selects a medium that has the maximum interval of the reference first of all. Since it maintains many popular media in the limited buffer space, the buffer hit ratio is increased. It also manages many service requests. As a result, our method improves the overall performance of the system. We compare the proposed method with the Priority-Hints method in terms of the buffer hit ratio and the deadline missing ratio of transactions. It is shown through the performance evaluation that our method outperforms the existing methods.