The design of the query is crucial for the performance of DETR and its
variants. Each query consists of two components: a content part and a
positional one. Traditionally, the content query is initialized with a zero or
learnable embedding, lacking essential content information and resulting in
sub-optimal performance. In this paper, we introduce a novel plug-and-play
module, Self-Adaptive Content Query (SACQ), to address this limitation. The
SACQ module utilizes features from the transformer encoder to generate content
queries via self-attention pooling. This allows candidate queries to adapt to
the input image, resulting in a more comprehensive content prior and better
focus on target objects. However, this improved concentration poses a challenge
for the training process that utilizes the Hungarian matching, which selects
only a single candidate and suppresses other similar ones. To overcome this, we
propose a query aggregation strategy to cooperate with SACQ. It merges similar
predicted candidates from different queries, easing the optimization. Our
extensive experiments on the COCO dataset demonstrate the effectiveness of our
proposed approaches across six different DETR's variants with multiple
configurations, achieving an average improvement of over 1.0 AP.

本文介绍了一种自适应内容查询模块（SACQ），它利用变换器编码器的特征通过自注意力池化生成内容查询，解决了传统零初始化或可学习嵌入的内容查询缺乏信息导致性能不佳的问题。为了应对增强的集中力对匈牙利匹配训练过程的挑战，我们提出了查询聚合策略，合并来自不同查询的类似预测候选项，从而简化优化过程。在 COCO 数据集上的广泛实验证明了我们所提方法的有效性，在多种配置下对六种 DETR 变体均取得了超过 1.0 AP 的平均改进。