论文标题
下文匹配的拼贴生成用于水下无脊椎动物检测
Context-Matched Collage Generation for Underwater Invertebrate Detection
论文作者
论文摘要
训练集的质量和大小通常会限制许多最先进的对象探测器的性能。但是，在许多情况下，很难收集培训图像，更不用说与收集适合训练这些对象探测器的注释相关的成本。由于这些原因，在具有挑战性的视频数据集（例如用于水下基板和无脊椎动物分析的数据集（DUSIA））中，预算只能允许收集和提供部分注释。为了帮助培训有限和部分注释相关的挑战，我们介绍了上下文匹配的拼贴画，该拼贴画利用明确的上下文标签将未使用的背景示例与现有注释数据相结合，以合成最终改善对象检测性能的其他培训样本。通过将一组生成的拼贴图像与原始训练集相结合，我们可以使用Dusia上的三个不同对象检测器来提高性能，最终在数据集中实现了最先进的对象检测性能。
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.
