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。

通过改进内容查询和相似查询聚合提升 DETR 变体性能

Enhancing DETRs Variants through Improved Content Query and Similar  Query Aggregation

Objective: Identifying study-eligible patients within clinical databases is a
critical step in clinical research. However, accurate query design typically
requires extensive technical and biomedical expertise. We sought to create a
system capable of generating data model-agnostic queries while also providing
novel logical reasoning capabilities for complex clinical trial eligibility
criteria.
Materials and Methods: The task of query creation from eligibility criteria
requires solving several text-processing problems, including named entity
recognition and relation extraction, sequence-to-sequence transformation,
normalization, and reasoning. We incorporated hybrid deep learning and
rule-based modules for these, as well as a knowledge base of the Unified
Medical Language System (UMLS) and linked ontologies. To enable data-model
agnostic query creation, we introduce a novel method for tagging database
schema elements using UMLS concepts. To evaluate our system, called LeafAI, we
compared the capability of LeafAI to a human database programmer to identify
patients who had been enrolled in 8 clinical trials conducted at our
institution. We measured performance by the number of actual enrolled patients
matched by generated queries.
Results: LeafAI matched a mean 43% of enrolled patients with 27,225 eligible
across 8 clinical trials, compared to 27% matched and 14,587 eligible in
queries by a human database programmer. The human programmer spent 26 total
hours crafting queries compared to several minutes by LeafAI.
Conclusions: Our work contributes a state-of-the-art data model-agnostic
query generation system capable of conditional reasoning using a knowledge
base. We demonstrate that LeafAI can rival a human programmer in finding
patients eligible for clinical trials.

利用深度学习和基于规则的模块生成具有新颖逻辑推理能力的、数据模型非相关性和基于 UMLS（Unified Medical Language System）的数据库查询方法，以帮助临床研究中的受试者筛选。使用 LeafAI 系统的查询结果表明，与人类数据库程序员相比，其找到的适宜受试者占比更高。

LeafAI: 临床队列发现的查询生成器，可与人类程序员媲美

LeafAI: query generator for clinical cohort discovery rivaling a human  programmer

In this paper, we propose a novel query design for the transformer-based
object detection. In previous transformer-based detectors, the object queries
are a set of learned embeddings. However, each learned embedding does not have
an explicit physical meaning and we cannot explain where it will focus on. It
is difficult to optimize as the prediction slot of each object query does not
have a specific mode. In other words, each object query will not focus on a
specific region. To solved these problems, in our query design, object queries
are based on anchor points, which are widely used in CNN-based detectors. So
each object query focuses on the objects near the anchor point. Moreover, our
query design can predict multiple objects at one position to solve the
difficulty: "one region, multiple objects". In addition, we design an attention
variant, which can reduce the memory cost while achieving similar or better
performance than the standard attention in DETR. Thanks to the query design and
the attention variant, the proposed detector that we called Anchor DETR, can
achieve better performance and run faster than the DETR with 10$\times$ fewer
training epochs. For example, it achieves 44.2 AP with 19 FPS on the MSCOCO
dataset when using the ResNet50-DC5 feature for training 50 epochs. Extensive
experiments on the MSCOCO benchmark prove the effectiveness of the proposed
methods. Code is available at
https://github.com/megvii-research/AnchorDETR.

本论文针对基于 Transformer 的物体检测算法，提出一种新的查询设计，纠正了目前算法中普遍存在的嵌入表达无法明确物体目标位置等缺陷，改为基于锚点查询方法，实现定位到特定区域及多目标检测的要求，并通过注意力变量方案来达到更好的算法性能。