# Anno-incomplete Multi-dataset Detection Source: [Anno-incomplete Multi-dataset Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:13:35.838Z Authors: Yiran Xu, Haoxiang Zhong, Kai Wu, Jialin Li, Yong Liu, Chengjie Wang, Shu-Tao Xia, Hongen Liao Published: 2024-08-29T03:58:21.000Z ## Abstract Object detectors have shown outstanding performance on various public datasets. However, annotating a new dataset for a new task is usually unavoidable in real, since 1) a single existing dataset usually does not contain all object categories needed; 2) using multiple datasets usually suffers from annotation incompletion and heterogeneous features. We propose a novel problem as "Annotation-incomplete Multi-dataset Detection", and develop an end-to-end multi-task learning architecture which can accurately detect all the object categories with multiple partially annotated datasets. Specifically, we propose an attention feature extractor which helps to mine the relations among different datasets. Besides, a knowledge amalgamation training strategy is incorporated to accommodate heterogeneous features from different sources. Extensive experiments on different object detection datasets demonstrate the effectiveness of our methods and an improvement of 2.17%, 2.10% in mAP can be achieved on COCO and VOC respectively. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()