Human-Object Interactions (HOI) detection, which aims to localize a human and
a relevant object while recognizing their interaction, is crucial for
understanding a still image. Recently, transformer-based models have
significantly advanced the progress of HOI detection. However, the capability
of these models has not been fully explored since the Object Query of the model
is always simply initialized as just zeros, which would affect the performance.
In this paper, we try to study the issue of promoting transformer-based HOI
detectors by initializing the Object Query with category-aware semantic
information. To this end, we innovatively propose the Category-Aware
Transformer Network (CATN). Specifically, the Object Query would be initialized
via category priors represented by an external object detection model to yield
better performance. Moreover, such category priors can be further used for
enhancing the representation ability of features via the attention mechanism.
We have firstly verified our idea via the Oracle experiment by initializing the
Object Query with the groundtruth category information. And then extensive
experiments have been conducted to show that a HOI detection model equipped
with our idea outperforms the baseline by a large margin to achieve a new
state-of-the-art result.

本篇论文介绍了一种利用类别感知信息初始化对象查询提升变压器基础 HOI 检测器性能的方法，即 Category-Aware Transformer Network (CATN)，并通过验证实验和大量实验表明，该方法可以显著提高 HOI 检测器的性能，从而实现新的最先进结果。

类别感知变压器网络用于更好的人物 - 物体交互检测

Category-Aware Transformer Network for Better Human-Object Interaction  Detection

Monocular 3D object detection has long been a challenging task in autonomous
driving, which requires to decode 3D predictions solely from a single 2D image.
Most existing methods follow conventional 2D object detectors to localize
objects based on their centers, and predict 3D attributes by neighboring
features around the centers. However, only using local features is insufficient
to understand the scene-level 3D spatial structures and ignore the inter-object
depth relations from contextual cues. In this paper, we introduce a novel
framework for Monocular DEtection with a depth-guided TRansformer, named
MonoDETR. The vanilla transformer is modified to be depth-aware and the whole
detection process is then guided by depth. Specifically, we represent 3D object
candidates as a set of queries and adopt an attention-based depth encoder to
produce non-local depth embeddings of the input image. Then, we propose a
depth-guided decoder with depth cross-attention modules to conduct both
inter-query and query-scene depth feature interactions. In this way, each
object query estimates its 3D attributes adaptively from the depth-guided
regions from the image and is no longer constrained to use only neighboring
visual features. MonoDETR is an end-to-end network without extra data or NMS
post-processing and achieves state-of-the-art performance on KITTI benchmark
with significant gains. Extensive ablation studies demonstrate the
effectiveness of our approach and its potential to serve as a transformer
baseline for future monocular 3D object detection research. Code is available
at this https URL.

本研究介绍了一种名为 MonoDETR 的使用深度感知 Transformer 的新型单目三维物体检测框架。通过将物体候选项表示为一组查询，并采用基于注意力的深度编码器生成二维输入图像的非局部深度嵌入，然后提出了一个深度引导的解码器，通过深度交叉关注模块对查询与场景深度特征进行交互，每个物体查询从图像的深度引导区域自适应地估计其三维属性。该系统具有端到端的结构，在 KITTI 基准测试上取得了最新成果，并在广泛的消融研究中证明了该方法的有效性，具有成为未来单目三维物体检测研究的 Transformer 基线的潜力。