# Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation Source: [Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:24:25.429Z Authors: Yiming Cui, Linjie Yang, Haichao Yu Published: 2023-07-23T06:26:27.000Z ## Abstract Transformer-based detection and segmentation methods use a list of learned detection queries to retrieve information from the transformer network and learn to predict the location and category of one specific object from each query. We empirically find that random convex combinations of the learned queries are still good for the corresponding models. We then propose to learn a convex combination with dynamic coefficients based on the high-level semantics of the image. The generated dynamic queries, named modulated queries, better capture the prior of object locations and categories in the different images. Equipped with our modulated queries, a wide range of DETR-based models achieve consistent and superior performance across multiple tasks including object detection, instance segmentation, panoptic segmentation, and video instance segmentation. ## 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. - [Object Detection on COCO 2017 (val)](): Object Detection; COCO 2017 (val); AP: 49.7. ## Original sources and implementation [Original paper]() [PDF]()