Salient object detection (SOD) is a crucial and preliminary task for many
computer vision applications, which have made progress with deep CNNs. Most of
the existing methods mainly rely on the RGB information to distinguish the
salient objects, which faces difficulties in some complex scenarios. To solve
this, many recent RGBD-based networks are proposed by adopting the depth map as
an independent input and fuse the features with RGB information. Taking the
advantages of RGB and RGBD methods, we propose a novel depth-aware salient
object detection framework, which has following superior designs: 1) It only
takes the depth information as training data while only relies on RGB
information in the testing phase. 2) It comprehensively optimizes SOD features
with multi-level depth-aware regularizations. 3) The depth information also
serves as error-weighted map to correct the segmentation process. With these
insightful designs combined, we make the first attempt in realizing an unified
depth-aware framework with only RGB information as input for inference, which
not only surpasses the state-of-the-art performances on five public RGB SOD
benchmarks, but also surpasses the RGBD-based methods on five benchmarks by a
large margin, while adopting less information and implementation
light-weighted. The code and model will be publicly available.

提出了一种新颖的深度感知显著目标检测框架，采用多层深度感知规则全面优化 SOD 特征，并将深度信息作为误差加权映射来修正分割过程，该框架在利用 RGB 信息作为输入进行推断时优于现有的 RGB SOD 方法和 RGBD 方法的性能表现，并具有轻量级的实现方式。

深度对醒目目标检测真正必需吗？

Is Depth Really Necessary for Salient Object Detection?

The best RGBD trackers provide high accuracy but are slow to run. On the
other hand, the best RGB trackers are fast but clearly inferior on the RGBD
datasets. In this work, we propose a deep depth-aware long-term tracker that
achieves state-of-the-art RGBD tracking performance and is fast to run. We
reformulate deep discriminative correlation filter (DCF) to embed the depth
information into deep features. Moreover, the same depth-aware correlation
filter is used for target re-detection. Comprehensive evaluations show that the
proposed tracker achieves state-of-the-art performance on the Princeton RGBD,
STC, and the newly-released CDTB benchmarks and runs 20 fps.

本文提出了基于深度感知的长期 RGBD 追踪算法，通过将深度信息嵌入到深度相关滤波器中，并用于目标重新检测，实现了与当前最先进 RGBD 追踪算法相当甚至更好的性能，运行速度达 20fps。

DAL -- 深度感知的长期跟踪器

DAL -- A Deep Depth-aware Long-term Tracker

Unsupervised domain adaptation (UDA) is important for applications where
large scale annotation of representative data is challenging. For semantic
segmentation in particular, it helps deploy on real "target domain" data models
that are trained on annotated images from a different "source domain", notably
a virtual environment. To this end, most previous works consider semantic
segmentation as the only mode of supervision for source domain data, while
ignoring other, possibly available, information like depth. In this work, we
aim at exploiting at best such a privileged information while training the UDA
model. We propose a unified depth-aware UDA framework that leverages in several
complementary ways the knowledge of dense depth in the source domain. As a
result, the performance of the trained semantic segmentation model on the
target domain is boosted. Our novel approach indeed achieves state-of-the-art
performance on different challenging synthetic-2-real benchmarks.

该工作提出了一种统一的深度感知无监督域自适应框架，通过在源领域中利用密集深度的多种补充方式，最大限度地利用了这种特权信息来训练 UDA 模型，从而提高了目标领域上训练的语义分割模型的性能，并在不同的具有挑战性的 synthetic-2-real 基准测试上实现了最先进的性能。