This work presents a drone detector with modified backbone and multiple
pyramid feature maps enhancement structure (MDDPE). Novel feature maps improve
modules that uses different levels of information to produce more robust and
discriminatory features is proposed. These module includes the feature maps
supplement function and the feature maps recombination enhancement function.To
effectively handle the drone characteristics, auxiliary supervisions that are
implemented in the early stages by employing tailored anchors designed are
utilized. To further improve the modeling of real drone detection scenarios and
initialization of the regressor, an updated anchor matching technique is
introduced to match anchors and ground truth drone as closely as feasible. To
show the proposed MDDPE's superiority over the most advanced detectors,
extensive experiments are carried out using well-known drone detection
benchmarks.

本研究提出了一种改进的骨干和多金字塔特征图增强结构 (MDDPE) 的无人机探测器。通过使用不同层次的信息产生更加稳健和区分性的特征的新颖特征映射改进模块，增加了骨干模块的性能。这些模块包括特征图的补充函数和特征图的重新组合增强函数。为了有效处理无人机的特征，使用定制的锚点设计在早期阶段采用辅助监督。为了进一步提高真实无人机检测场景的建模和回归器的初始化，引入了一种更新的锚点匹配技术，使锚点与实际情况尽可能地匹配。通过使用众所周知的无人机检测基准进行了大量实验，证明了所提出的 MDDPE 相对于最先进的检测器的优越性。

具有改进骨干网络和多金字塔特征图增强结构的无人机探测器

A drone detector with modified backbone and multiple pyramid featuremaps  enhancement structure (MDDPE)

While 3D body reconstruction methods have made remarkable progress recently,
it remains difficult to acquire the sufficiently accurate and numerous 3D
supervisions required for training. In this paper, we propose \textbf{KNOWN}, a
framework that effectively utilizes body \textbf{KNOW}ledge and
u\textbf{N}certainty modeling to compensate for insufficient 3D supervisions.
KNOWN exploits a comprehensive set of generic body constraints derived from
well-established body knowledge. These generic constraints precisely and
explicitly characterize the reconstruction plausibility and enable 3D
reconstruction models to be trained without any 3D data. Moreover, existing
methods typically use images from multiple datasets during training, which can
result in data noise (\textit{e.g.}, inconsistent joint annotation) and data
imbalance (\textit{e.g.}, minority images representing unusual poses or
captured from challenging camera views). KNOWN solves these problems through a
novel probabilistic framework that models both aleatoric and epistemic
uncertainty. Aleatoric uncertainty is encoded in a robust Negative
Log-Likelihood (NLL) training loss, while epistemic uncertainty is used to
guide model refinement. Experiments demonstrate that KNOWN's body
reconstruction outperforms prior weakly-supervised approaches, particularly on
the challenging minority images.

利用已知知识和不确定性建模以弥补不足的三维监督的 3D 人体重建方法

单目三维人体重建的身体知识和不确定性建模

Body Knowledge and Uncertainty Modeling for Monocular 3D Human Body  Reconstruction

We address the problem of video grounding from natural language queries. The
key challenge in this task is that one training video might only contain a few
annotated starting/ending frames that can be used as positive examples for
model training. Most conventional approaches directly train a binary classifier
using such imbalance data, thus achieving inferior results. The key idea of
this paper is to use the distances between the frame within the ground truth
and the starting (ending) frame as dense supervisions to improve the video
grounding accuracy. Specifically, we design a novel dense regression network
(DRN) to regress the distances from each frame to the starting (ending) frame
of the video segment described by the query. We also propose a simple but
effective IoU regression head module to explicitly consider the localization
quality of the grounding results (i.e., the IoU between the predicted location
and the ground truth). Experimental results show that our approach
significantly outperforms state-of-the-arts on three datasets (i.e.,
Charades-STA, ActivityNet-Captions, and TACoS).

本研究旨在解决自然语言查询的视频地面问题，并通过使用密集监管改进视频地面的准确性，相比与现有方法，我们的方法在三个数据集上均有明显的优势。

视频定位用的密集回归网络

Dense Regression Network for Video Grounding

Text style transfer aims to modify the style of a sentence while keeping its
content unchanged. Recent style transfer systems often fail to faithfully
preserve the content after changing the style. This paper proposes a structured
content preserving model that leverages linguistic information in the
structured fine-grained supervisions to better preserve the style-independent
content during style transfer. In particular, we achieve the goal by devising
rich model objectives based on both the sentence's lexical information and a
language model that conditions on content. The resulting model therefore is
encouraged to retain the semantic meaning of the target sentences. We perform
extensive experiments that compare our model to other existing approaches in
the tasks of sentiment and political slant transfer. Our model achieves
significant improvement in terms of both content preservation and style
transfer in automatic and human evaluation.

本文提出了一种基于结构化细粒度监督的内容保留模型，利用语言信息并设定模型目标以达到在改变句子的风格的同时更好地保留与风格无关的内容，进行情感和政治倾向转换任务的实验表明该模型在内容保留和风格转换上表现出显著改善。