The quality and size of training sets often limit the performance of many state of the art object detectors. However, in many scenarios, it can be difficult to collect images for training, not to mention the costs associated with collecting annotations suitable for training these object detectors. For these reasons, on challenging video datasets such as the Dataset for Underwater Substrate and Invertebrate Analysis (DUSIA), budgets may only allow for collecting and providing partial annotations. To aid in the challenges associated with training with limited and partial annotations, we introduce Context Matched Collages, which leverage explicit context labels to combine unused background examples with existing annotated data to synthesize additional training samples that ultimately improve object detection performance. By combining a set of our generated collage images with the original training set, we see improved performance using three different object detectors on DUSIA, ultimately achieving state of the art object detection performance on the dataset.

翻译：由于这些原因,在具有挑战性的视频数据集方面,如水下基底和脊椎动物分析数据集(DUSIA),预算可能只允许收集和提供部分说明。为了帮助应对与有限和部分说明的培训相关的挑战,我们引入了环境匹配拼贴标签,以利用明确的上下文标签,将未使用的背景示例与现有的附加说明的数据结合起来,以综合其他培训样本,从而最终改进物体探测性能。通过将我们制作的一组拼贴图像与最初的成套培训图像结合起来,我们看到在DUSIA上使用三种不同的物体探测器的性能有所改善,最终在数据集上实现艺术物体探测性能的状态。