Detection Transformer (DETR) and its variants have shown great potential for
accurate object detection in recent years. The mechanism of object query
enables DETR family to directly obtain a fixed number of object predictions and
streamlines the detection pipeline. Meanwhile, recent studies also reveal that
with proper architecture design, convolution networks (ConvNets) also achieve
competitive performance with transformers, \eg, ConvNeXt. To this end, in this
paper we explore whether we could build a query-based end-to-end object
detection framework with ConvNets instead of sophisticated transformer
architecture. The proposed framework, \ie, Detection ConvNet (DECO), is
composed of a backbone and convolutional encoder-decoder architecture. We
carefully design the DECO encoder and propose a novel mechanism for our DECO
decoder to perform interaction between object queries and image features via
convolutional layers. We compare the proposed DECO against prior detectors on
the challenging COCO benchmark. Despite its simplicity, our DECO achieves
competitive performance in terms of detection accuracy and running speed.
Specifically, with the ResNet-50 and ConvNeXt-Tiny backbone, DECO obtains
$38.6\%$ and $40.8\%$ AP on COCO \textit{val} set with $35$ and $28$ FPS
respectively and outperforms the DETR model. Incorporated with advanced
multi-scale feature module, our DECO+ achieves $47.8\%$ AP with $34$ FPS. We
hope the proposed DECO brings another perspective for designing object
detection framework.

通过构建一个基于查询的以卷积网络为基础的端到端目标检测框架 DECO，本研究探索了是否能够用卷积网络来取代复杂的 Transformer 架构，该框架在复杂性较低的情况下实现了与传统的物体检测模型相媲美的性能，并带来了另一种设计目标检测框架的视角。

DECO: 基于查询的端到端目标检测与卷积神经网络

DECO: Query-Based End-to-End Object Detection with ConvNets

Understanding how humans use physical contact to interact with the world is
key to enabling human-centric artificial intelligence. While inferring 3D
contact is crucial for modeling realistic and physically-plausible human-object
interactions, existing methods either focus on 2D, consider body joints rather
than the surface, use coarse 3D body regions, or do not generalize to
in-the-wild images. In contrast, we focus on inferring dense, 3D contact
between the full body surface and objects in arbitrary images. To achieve this,
we first collect DAMON, a new dataset containing dense vertex-level contact
annotations paired with RGB images containing complex human-object and
human-scene contact. Second, we train DECO, a novel 3D contact detector that
uses both body-part-driven and scene-context-driven attention to estimate
vertex-level contact on the SMPL body. DECO builds on the insight that human
observers recognize contact by reasoning about the contacting body parts, their
proximity to scene objects, and the surrounding scene context. We perform
extensive evaluations of our detector on DAMON as well as on the RICH and
BEHAVE datasets. We significantly outperform existing SOTA methods across all
benchmarks. We also show qualitatively that DECO generalizes well to diverse
and challenging real-world human interactions in natural images. The code,
data, and models are available at this https URL

理解人类如何通过身体接触与世界互动对于实现以人为中心的人工智能至关重要。我们关注于推断全身表面与任意图像中的物体之间的密集 3D 接触，通过构建新的数据集 DAMON 以及训练新的 3D 接触检测器 DECO，在各种基准测试中显著优于现有方法，同时展示 DECO 在自然图像中广泛适用于多样且具有挑战性的真实人类互动。