Abstract
We explore a situation in which the target domain is accessible, but real-time data annotation is not feasible. Instead, we would like to construct an alternative training set from a large-scale data server so that a competitive model can be obtained. For this problem, because the target domain usually exhibits distinct modes (i.e., semantic clusters representing data distribution), if the training set does not contain these target modes, the model performance would be compromised. While prior existing works improve algorithms iteratively, our research explores the often-overlooked potential of optimizing the structure of the data server. Inspired by the hierarchical nature of web search engines, we introduce a hierarchical data server, together with a bipartite mode matching algorithm (BMM) to align source and target modes. For each target mode, we look in the server data tree for the best mode match, which might be large or small in size. Through bipartite matching, we aim for all target modes to be optimally matched with source modes in a one-on-one fashion. Compared with existing training set search algorithms, we show that the matched server modes constitute training sets that have consistently smaller domain gaps with the target domain across object re-identification (re-ID) and detection tasks. Consequently, models trained on our searched training sets have higher accuracy than those trained otherwise. BMM allows data-centric unsupervised domain adaptation (UDA) orthogonal to existing model-centric UDA methods. By combining the BMM with existing UDA methods like pseudo-labeling, further improvement is observed.
Abstract (translated)
我们探讨了一种情况，即目标领域可以访问，但实时数据标注不可行。为此，我们希望从大规模的数据服务器中构建一个替代的训练集，从而获得具有竞争力的模型。对于这个问题，因为目标域通常表现出不同的模式（即代表数据分布的语义簇），如果训练集中不包含这些目标模式，那么模型性能将受到影响。 以往的研究工作通过迭代改进算法来解决这一问题，而我们的研究则探索了往往被忽视的数据服务器结构优化潜力。受网络搜索引擎层次结构的启发，我们引入了一个分层数据服务器，并结合双轨模式匹配算法（BMM）以对齐源域和目标域的模式。对于每个目标模式，在服务器数据树中寻找最佳模式匹配，无论其大小如何。通过双轨匹配，我们的目的是让所有目标模式都能与源模式一对一地实现最优匹配。 相较于现有的训练集搜索算法，我们展示了所匹配的服务器模式构成的训练集在跨对象重识别（re-ID）和检测任务中的域差距始终较小。因此，在使用我们搜索到的训练集进行训练后，模型具有更高的准确性。 BMM使数据为中心的无监督领域适应（UDA）方法与现有以模型为中心的UDA方法相辅相成。通过结合BMM和其他现有的UDA方法（如伪标签），进一步改进了性能。
URL
https://arxiv.org/abs/2601.09531
