In this work, we present efficient modulation, a novel design for efficient
vision networks. We revisit the modulation mechanism, which operates input
through convolutional context modeling and feature projection layers, and fuses
features via element-wise multiplication and an MLP block. We demonstrate that
the modulation mechanism is particularly well suited for efficient networks and
further tailor the modulation design by proposing the efficient modulation
(EfficientMod) block, which is considered the essential building block for our
networks. Benefiting from the prominent representational ability of modulation
mechanism and the proposed efficient design, our network can accomplish better
trade-offs between accuracy and efficiency and set new state-of-the-art
performance in the zoo of efficient networks. When integrating EfficientMod
with the vanilla self-attention block, we obtain the hybrid architecture which
further improves the performance without loss of efficiency. We carry out
comprehensive experiments to verify EfficientMod's performance. With fewer
parameters, our EfficientMod-s performs 0.6 top-1 accuracy better than
EfficientFormerV2-s2 and is 25% faster on GPU, and 2.9 better than
MobileViTv2-1.0 at the same GPU latency. Additionally, our method presents a
notable improvement in downstream tasks, outperforming EfficientFormerV2-s by
3.6 mIoU on the ADE20K benchmark. Code and checkpoints are available at
this https URL

通过高效调制机制和高效调制设计，本研究提出一种高效视觉网络来在准确性和效率之间取得更好的折衷，并在有效网络中创造了最新的最佳性能。

高效视觉网络调制

Efficient Modulation for Vision Networks

Improving the aesthetic quality of images is challenging and eager for the
public. To address this problem, most existing algorithms are based on
supervised learning methods to learn an automatic photo enhancer for paired
data, which consists of low-quality photos and corresponding expert-retouched
versions. However, the style and characteristics of photos retouched by experts
may not meet the needs or preferences of general users. In this paper, we
present an unsupervised image enhancement generative adversarial network
(UEGAN), which learns the corresponding image-to-image mapping from a set of
images with desired characteristics in an unsupervised manner, rather than
learning on a large number of paired images. The proposed model is based on
single deep GAN which embeds the modulation and attention mechanisms to capture
richer global and local features. Based on the proposed model, we introduce two
losses to deal with the unsupervised image enhancement: (1) fidelity loss,
which is defined as a L2 regularization in the feature domain of a pre-trained
VGG network to ensure the content between the enhanced image and the input
image is the same, and (2) quality loss that is formulated as a relativistic
hinge adversarial loss to endow the input image the desired characteristics.
Both quantitative and qualitative results show that the proposed model
effectively improves the aesthetic quality of images. Our code is available at:
this https URL

本文提出了一种基于 GAN 的无监督图像增强模型 (UEGAN)，利用调制机制和质量损失函数增强图像的视觉质量，消除专业照片编辑造成的风格化问题。我们的质量和定量实验验证了我们的模型的有效性。

基于生成对抗网络的无监督深度图像增强技术

Towards Unsupervised Deep Image Enhancement with Generative Adversarial  Network

The superior performance of Deformable Convolutional Networks arises from its
ability to adapt to the geometric variations of objects. Through an examination
of its adaptive behavior, we observe that while the spatial support for its
neural features conforms more closely than regular ConvNets to object
structure, this support may nevertheless extend well beyond the region of
interest, causing features to be influenced by irrelevant image content. To
address this problem, we present a reformulation of Deformable ConvNets that
improves its ability to focus on pertinent image regions, through increased
modeling power and stronger training. The modeling power is enhanced through a
more comprehensive integration of deformable convolution within the network,
and by introducing a modulation mechanism that expands the scope of deformation
modeling. To effectively harness this enriched modeling capability, we guide
network training via a proposed feature mimicking scheme that helps the network
to learn features that reflect the object focus and classification power of
R-CNN features. With the proposed contributions, this new version of Deformable
ConvNets yields significant performance gains over the original model and
produces leading results on the COCO benchmark for object detection and
instance segmentation.

通过增强 Deformable Convolutional Networks 的变形卷积能力、引入调制机制和特征模仿方案，提高了其在目标检测和实例分割任务中的性能表现。