We introduce a novel bilateral reference framework (***BiRefNet***) for
high-resolution dichotomous image segmentation (DIS). It comprises two
essential components: the localization module (LM) and the reconstruction
module (RM) with our proposed bilateral reference (BiRef). The LM aids in
object localization using global semantic information. Within the RM, we
utilize BiRef for the reconstruction process, where hierarchical patches of
images provide the source reference and gradient maps serve as the target
reference. These components collaborate to generate the final predicted maps.
We also introduce auxiliary gradient supervision to enhance focus on regions
with finer details. Furthermore, we outline practical training strategies
tailored for DIS to improve map quality and training process. To validate the
general applicability of our approach, we conduct extensive experiments on four
tasks to evince that *BiRefNet* exhibits remarkable performance, outperforming
task-specific cutting-edge methods across all benchmarks.

我们引入了一种新的双边参考框架（BiRefNet），用于高分辨率的二分图像分割（DIS），通过全局语义信息的方式来进行对象定位，利用双边参考进行图像重建，并引入辅助梯度监督来提高对细节区域的关注，这种方法在多个任务中展现非凡的性能，超过了各种基准上的最新方法。

高分辨率二鉴图像分割的双边参考

Bilateral Reference for High-Resolution Dichotomous Image Segmentation

Whole-body pose and shape estimation aims to jointly predict different
behaviors (e.g., pose, hand gesture, facial expression) of the entire human
body from a monocular image. Existing methods often exhibit degraded
performance under the complexity of in-the-wild scenarios. We argue that the
accuracy and reliability of these models are significantly affected by the
quality of the predicted \textit{bounding box}, e.g., the scale and alignment
of body parts. The natural discrepancy between the ideal bounding box
annotations and model detection results is particularly detrimental to the
performance of whole-body pose and shape estimation. In this paper, we propose
a novel framework to enhance the robustness of whole-body pose and shape
estimation. Our framework incorporates three new modules to address the above
challenges from three perspectives: \textbf{1) Localization Module} enhances
the model's awareness of the subject's location and semantics within the image
space. \textbf{2) Contrastive Feature Extraction Module} encourages the model
to be invariant to robust augmentations by incorporating contrastive loss with
dedicated positive samples. \textbf{3) Pixel Alignment Module} ensures the
reprojected mesh from the predicted camera and body model parameters are
accurate and pixel-aligned. We perform comprehensive experiments to demonstrate
the effectiveness of our proposed framework on body, hands, face and whole-body
benchmarks. Codebase is available at
https://github.com/robosmplx/robosmplx.

我们提出了一个新的框架来增强全身姿势和形状估计的鲁棒性，其中包括三个模块以从三个角度解决上述挑战：1）定位模块，2）对比特征提取模块，3）像素对齐模块，并通过全面实验证明了该框架在身体、手、脸和全身基准测试上的有效性。

走向强健而富表现力的全身人体姿势和形状估计

Towards Robust and Expressive Whole-body Human Pose and Shape Estimation

We propose an end-to-end deep convolutional network to simultaneously
localize and rank relative visual attributes, given only weakly-supervised
pairwise image comparisons. Unlike previous methods, our network jointly learns
the attribute's features, localization, and ranker. The localization module of
our network discovers the most informative image region for the attribute,
which is then used by the ranking module to learn a ranking model of the
attribute. Our end-to-end framework also significantly speeds up processing and
is much faster than previous methods. We show state-of-the-art ranking results
on various relative attribute datasets, and our qualitative localization
results clearly demonstrate our network's ability to learn meaningful image
patches.

提出了一个端到端的深度卷积神经网络，可以同时定位和排序相对视觉属性，在仅使用弱监督成对图像比较的情况下进行学习。不同于之前的方法，我们的网络联合学习了特征、定位和排序。我们的定位模块发现属性最有信息量的图像区域，这个区域被排序模块用来学习属性的排序模型。我们的端到端框架也显著加速了处理速度，比之前的方法快得多。我们在各种相对属性数据集上展示了最先进的排序结果，我们的定位结果清楚地表明了我们的网络学习了有意义的图像块。