Accurate 3D shape abstraction from a single 2D image is a long-standing
problem in computer vision and graphics. By leveraging a set of primitives to
represent the target shape, recent methods have achieved promising results.
However, these methods either use a relatively large number of primitives or
lack geometric flexibility due to the limited expressibility of the primitives.
In this paper, we propose a novel bi-channel Transformer architecture,
integrated with parameterized deformable models, termed DeFormer, to
simultaneously estimate the global and local deformations of primitives. In
this way, DeFormer can abstract complex object shapes while using a small
number of primitives which offer a broader geometry coverage and finer details.
Then, we introduce a force-driven dynamic fitting and a cycle-consistent
re-projection loss to optimize the primitive parameters. Extensive experiments
on ShapeNet across various settings show that DeFormer achieves better
reconstruction accuracy over the state-of-the-art, and visualizes with
consistent semantic correspondences for improved interpretability.

本文提出了一种名为 DeFormer 的新型双通道 Transformer 架构，通过参数化的可变形模型同时估计原语的全局和局部变形，以实现复杂对象形状的抽象化，在使用较少的原语的同时提供更广泛的几何覆盖范围和更精细的细节，并引入了力驱动的动态拟合和循环一致的投影损失来优化原语参数，在 ShapeNet 上的广泛实验表明，DeFormer 在重建准确性方面优于现有技术，并且通过一致的语义对应关系实现了可解释性的提升。

DeFormer: 集成可变形模型的变压器用于从单张图片中进行三维形状抽象

DeFormer: Integrating Transformers with Deformable Models for 3D Shape  Abstraction from a Single Image

The goal of low-light image enhancement is to restore the color and details
of the image and is of great significance for high-level visual tasks in
autonomous driving. However, it is difficult to restore the lost details in the
dark area by relying only on the RGB domain. In this paper we introduce
frequency as a new clue into the network and propose a novel DCT-driven
enhancement transformer (DEFormer). First, we propose a learnable frequency
branch (LFB) for frequency enhancement contains DCT processing and
curvature-based frequency enhancement (CFE). CFE calculates the curvature of
each channel to represent the detail richness of different frequency bands,
then we divides the frequency features, which focuses on frequency bands with
richer textures. In addition, we propose a cross domain fusion (CDF) for
reducing the differences between the RGB domain and the frequency domain. We
also adopt DEFormer as a preprocessing in dark detection, DEFormer effectively
improves the performance of the detector, bringing 2.1% and 3.4% improvement in
ExDark and DARK FACE datasets on mAP respectively.

该研究论文介绍了一种利用频域作为图像增强的新方法，提出了一种新颖的基于 DCT 的增强转换器（DEFormer），其中包括可学习的频率支路（LFB）和交叉域融合（CDF），并将其应用于暗部检测，使检测器的性能显著提升。

DEFormer：低光图像和暗视觉的 DCT 驱动增强 Transformer

DEFormer: DCT-driven Enhancement Transformer for Low-light Image and  Dark Vision

Transformer-based QA models use input-wide self-attention -- i.e. across both
the question and the input passage -- at all layers, causing them to be slow
and memory-intensive. It turns out that we can get by without input-wide
self-attention at all layers, especially in the lower layers. We introduce
DeFormer, a decomposed transformer, which substitutes the full self-attention
with question-wide and passage-wide self-attentions in the lower layers. This
allows for question-independent processing of the input text representations,
which in turn enables pre-computing passage representations reducing runtime
compute drastically. Furthermore, because DeFormer is largely similar to the
original model, we can initialize DeFormer with the pre-training weights of a
standard transformer, and directly fine-tune on the target QA dataset. We show
DeFormer versions of BERT and XLNet can be used to speed up QA by over 4.3x and
with simple distillation-based losses they incur only a 1% drop in accuracy. We
open source the code at this https URL

DeFormer 是一种改进后的基于 Transformer 的问答模型，通过在低层使用问题和输入问句的局部自注意力代替全局自注意力，实现输入文本表示的与问题无关处理，从而可以预计算表示以减少运行时间，并增加简单蒸馏损失来提高速度，仅有 1% 的准确率下降。