Color-guided depth super-resolution (DSR) is an encouraging paradigm that
enhances a low-resolution (LR) depth map guided by an extra high-resolution
(HR) RGB image from the same scene. Existing methods usually use interpolation
to upscale the depth maps before feeding them into the network and transfer the
high-frequency information extracted from HR RGB images to guide the
reconstruction of depth maps. However, the extracted high-frequency information
usually contains textures that are not present in depth maps in the existence
of the cross-modality gap, and the noises would be further aggravated by
interpolation due to the resolution gap between the RGB and depth images. To
tackle these challenges, we propose a novel Symmetric Uncertainty-aware Feature
Transmission (SUFT) for color-guided DSR. (1) For the resolution gap, SUFT
builds an iterative up-and-down sampling pipeline, which makes depth features
and RGB features spatially consistent while suppressing noise amplification and
blurring by replacing common interpolated pre-upsampling. (2) For the
cross-modality gap, we propose a novel Symmetric Uncertainty scheme to remove
parts of RGB information harmful to the recovery of HR depth maps. Extensive
experiments on benchmark datasets and challenging real-world settings suggest
that our method achieves superior performance compared to state-of-the-art
methods. Our code and models are available at
this https URL

本文提出了一种新颖的对称不确定性特征传输（SUFT）来解决深度图超分辨率中颜色指导的挑战，包括分辨率差异和跨模态差异，并取得了优于现有方法的表现。

深度超分辨率对称不确定性感知特征传输

Symmetric Uncertainty-Aware Feature Transmission for Depth  Super-Resolution

RGB-Infrared (RGB-IR) person re-identification (ReID) is a technology where
the system can automatically identify the same person appearing at different
parts of a video when light is unavailable. The critical challenge of this task
is the cross-modality gap of features under different modalities. To solve this
challenge, we proposed a Teacher-Student GAN model (TS-GAN) to adopt different
domains and guide the ReID backbone to learn better ReID information. (1) In
order to get corresponding RGB-IR image pairs, the RGB-IR Generative
Adversarial Network (GAN) was used to generate IR images. (2) To kick-start the
training of identities, a ReID Teacher module was trained under IR modality
person images, which is then used to guide its Student counterpart in training.
(3) Likewise, to better adapt different domain features and enhance model ReID
performance, three Teacher-Student loss functions were used. Unlike other GAN
based models, the proposed model only needs the backbone module at the test
stage, making it more efficient and resource-saving. To showcase our model's
capability, we did extensive experiments on the newly-released SYSU-MM01 RGB-IR
Re-ID benchmark and achieved superior performance to the state-of-the-art with
49.8% Rank-1 and 47.4% mAP.

利用基于 RGB-IR 的深度学习模型，通过两个 Teacher-Student GAN 模块相互引导，成功实现了在无光照条件下的人物跨域识别。在 SYSU-MM01 RGB-IR Re-ID 基准测试中，取得了 49.8% 的顶级成绩和 47.4% 的平均精度。