Utilizing dataset affinity prediction in object detection to assess training data
This addresses the challenge of principled dataset pooling for object detection, but it is incremental as it builds on standard pipelines with a new module.
The paper tackled the problem of assessing the effectiveness of pooling datasets for object detection by proposing a dataset affinity prediction module. The result showed that object detectors could be trained on a significantly sparser set of samples without losing detection accuracy.
Data pooling offers various advantages, such as increasing the sample size, improving generalization, reducing sampling bias, and addressing data sparsity and quality, but it is not straightforward and may even be counterproductive. Assessing the effectiveness of pooling datasets in a principled manner is challenging due to the difficulty in estimating the overall information content of individual datasets. Towards this end, we propose incorporating a data source prediction module into standard object detection pipelines. The module runs with minimal overhead during inference time, providing additional information about the data source assigned to individual detections. We show the benefits of the so-called dataset affinity score by automatically selecting samples from a heterogeneous pool of vehicle datasets. The results show that object detectors can be trained on a significantly sparser set of training samples without losing detection accuracy.