Detecting objects from Unmanned Aerial Vehicles (UAV) is often hindered by a
large number of small objects, resulting in low detection accuracy. To address
this issue, mainstream approaches typically utilize multi-stage inferences.
Despite their remarkable detecting accuracies, real-time efficiency is
sacrificed, making them less practical to handle real applications. To this
end, we propose to improve the single-stage inference accuracy through learning
scale-invariant features. Specifically, a Scale-Invariant Feature Disentangling
module is designed to disentangle scale-related and scale-invariant features.
Then an Adversarial Feature Learning scheme is employed to enhance
disentanglement. Finally, scale-invariant features are leveraged for robust
UAV-based object detection. Furthermore, we construct a multi-modal UAV object
detection dataset, State-Air, which incorporates annotated UAV state
parameters. We apply our approach to three state-of-the-art lightweight
detection frameworks on three benchmark datasets, including State-Air.
Extensive experiments demonstrate that our approach can effectively improve
model accuracy. Our code and dataset are provided in Supplementary Materials
and will be publicly available once the paper is accepted.

通过学习无人机对象检测的尺度不变特征，提出了一种改进单阶段推理准确性的方法，并设计了尺度不变特征分离模块和对抗特征学习方案来增强分离，最终通过使用尺度不变特征来实现鲁棒的无人机对象检测。

无人机目标检测中基于对抗学习的尺度不变特征解缠

Scale-Invariant Feature Disentanglement via Adversarial Learning for  UAV-based Object Detection

Monocular depth estimation (MDE) in the self-supervised scenario has emerged
as a promising method as it refrains from the requirement of ground truth
depth. Despite continuous efforts, MDE is still sensitive to scale changes
especially when all the training samples are from one single camera. Meanwhile,
it deteriorates further since camera movement results in heavy coupling between
the predicted depth and the scale change. In this paper, we present a
scale-invariant approach for self-supervised MDE, in which scale-sensitive
features (SSFs) are detached away while scale-invariant features (SIFs) are
boosted further. To be specific, a simple but effective data augmentation by
imitating the camera zooming process is proposed to detach SSFs, making the
model robust to scale changes. Besides, a dynamic cross-attention module is
designed to boost SIFs by fusing multi-scale cross-attention features
adaptively. Extensive experiments on the KITTI dataset demonstrate that the
detaching and boosting strategies are mutually complementary in MDE and our
approach achieves new State-of-The-Art performance against existing works from
0.097 to 0.090 w.r.t absolute relative error. The code will be made public
soon.

本研究提出了一种自监督的单目深度估计方法，通过剥离具有尺度敏感性的特征和采取仿真相机缩放的数据增强方法来提高尺度不变性，并使用动态跨尺度交叉注意力模块进一步加强尺度不变特征，在 KITTI 数据集上取得了目前最佳性能。

分离和增强：用于尺度不变自监督单目深度估计的双引擎

Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised  Monocular Depth Estimation

Feature pyramid has been an efficient method to extract features at different
scales. Development over this method mainly focuses on aggregating contextual
information at different levels while seldom touching the inter-level
correlation in the feature pyramid. Early computer vision methods extracted
scale-invariant features by locating the feature extrema in both spatial and
scale dimension. Inspired by this, a convolution across the pyramid level is
proposed in this study, which is termed pyramid convolution and is a modified
3-D convolution. Stacked pyramid convolutions directly extract 3-D (scale and
spatial) features and outperforms other meticulously designed feature fusion
modules. Based on the viewpoint of 3-D convolution, an integrated batch
normalization that collects statistics from the whole feature pyramid is
naturally inserted after the pyramid convolution. Furthermore, we also show
that the naive pyramid convolution, together with the design of RetinaNet head,
actually best applies for extracting features from a Gaussian pyramid, whose
properties can hardly be satisfied by a feature pyramid. In order to alleviate
this discrepancy, we build a scale-equalizing pyramid convolution (SEPC) that
aligns the shared pyramid convolution kernel only at high-level feature maps.
Being computationally efficient and compatible with the head design of most
single-stage object detectors, the SEPC module brings significant performance
improvement ($>4$AP increase on MS-COCO2017 dataset) in state-of-the-art
one-stage object detectors, and a light version of SEPC also has $\sim3.5$AP
gain with only around 7% inference time increase. The pyramid convolution also
functions well as a stand-alone module in two-stage object detectors and is
able to improve the performance by $\sim2$AP. The source code can be found at
this https URL

本研究主要探讨了特征金字塔与金字塔卷积在计算机视觉和目标检测中的应用，提出了一种可提高性能的尺度均衡金字塔卷积模块，并展示了其在单阶段和双阶段目标检测器上的优越表现.