Dichotomous Image Segmentation (DIS) has recently emerged towards
high-precision object segmentation from high-resolution natural images.
When designing an effective DIS model, the main challenge is how to balance
the semantic dispersion of high-resolution targets in the small receptive field
and the loss of high-precision details in the large receptive field. Existing
methods rely on tedious multiple encoder-decoder streams and stages to
gradually complete the global localization and local refinement.
Human visual system captures regions of interest by observing them from
multiple views. Inspired by it, we model DIS as a multi-view object perception
problem and provide a parsimonious multi-view aggregation network (MVANet),
which unifies the feature fusion of the distant view and close-up view into a
single stream with one encoder-decoder structure. With the help of the proposed
multi-view complementary localization and refinement modules, our approach
established long-range, profound visual interactions across multiple views,
allowing the features of the detailed close-up view to focus on highly slender
structures.Experiments on the popular DIS-5K dataset show that our MVANet
significantly outperforms state-of-the-art methods in both accuracy and speed.
The source code and datasets will be publicly available at
\href{this https URL}{MVANet}.

Dichotomous Image Segmentation (DIS) explores the challenge of balancing semantic dispersion and high-precision details in object segmentation. The paper proposes a parsimonious multi-view aggregation network (MVANet) that surpasses state-of-the-art methods in accuracy and speed.

多视角聚合网络用于二分图像分割

Multi-view Aggregation Network for Dichotomous Image Segmentation

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

High-accuracy Dichotomous Image Segmentation (DIS) aims to pinpoint
category-agnostic foreground objects from natural scenes. The main challenge
for DIS involves identifying the highly accurate dominant area while rendering
detailed object structure. However, directly using a general encoder-decoder
architecture may result in an oversupply of high-level features and neglect the
shallow spatial information necessary for partitioning meticulous structures.
To fill this gap, we introduce a novel Unite-Divide-Unite Network (UDUN} that
restructures and bipartitely arranges complementary features to simultaneously
boost the effectiveness of trunk and structure identification. The proposed
UDUN proceeds from several strengths. First, a dual-size input feeds into the
shared backbone to produce more holistic and detailed features while keeping
the model lightweight. Second, a simple Divide-and-Conquer Module (DCM) is
proposed to decouple multiscale low- and high-level features into our structure
decoder and trunk decoder to obtain structure and trunk information
respectively. Moreover, we design a Trunk-Structure Aggregation module (TSA) in
our union decoder that performs cascade integration for uniform high-accuracy
segmentation. As a result, UDUN performs favorably against state-of-the-art
competitors in all six evaluation metrics on overall DIS-TE, i.e., achieving
0.772 weighted F-measure and 977 HCE. Using 1024*1024 input, our model enables
real-time inference at 65.3 fps with ResNet-18.

高准确率的二值图像分割（DIS）旨在从自然场景中定位与类别无关的前景对象。本文提出了一种新颖的 Unite-Divide-Unite Network（UDUN）来提高主干和结构识别的效果，实现统一的高准确度分割。

统合 - 分离 - 统合：高准确性二分图像分割的联合增强主干和结构

Unite-Divide-Unite: Joint Boosting Trunk and Structure for High-accuracy  Dichotomous Image Segmentation

We present a systematic study on a new task called dichotomous image
segmentation (DIS) , which aims to segment highly accurate objects from natural
images. To this end, we collected the first large-scale DIS dataset, called
DIS5K, which contains 5,470 high-resolution (e.g., 2K, 4K or larger) images
covering camouflaged, salient, or meticulous objects in various backgrounds.
DIS is annotated with extremely fine-grained labels. Besides, we introduce a
simple intermediate supervision baseline (IS-Net) using both feature-level and
mask-level guidance for DIS model training. IS-Net outperforms various
cutting-edge baselines on the proposed DIS5K, making it a general self-learned
supervision network that can facilitate future research in DIS. Further, we
design a new metric called human correction efforts (HCE) which approximates
the number of mouse clicking operations required to correct the false positives
and false negatives. HCE is utilized to measure the gap between models and
real-world applications and thus can complement existing metrics. Finally, we
conduct the largest-scale benchmark, evaluating 16 representative segmentation
models, providing a more insightful discussion regarding object complexities,
and showing several potential applications (e.g., background removal, art
design, 3D reconstruction). Hoping these efforts can open up promising
directions for both academic and industries. Project page:
this https URL

本文研究了一项新任务，称为二元图像分割（DIS），目的是从自然图像中高精度地分割对象。我们收集了第一个大规模的 DIS 数据集，称为 DIS5K，包含 5470 个高分辨率图像，并使用极细粒度标签进行了注释。我们提出了一个简单的中间监督基线（IS-Net），用于 DIS 模型训练，并介绍了一个新的度量标准（HCE），用于衡量模型与实际应用之间的差距。最后，我们进行了最大规模的基准测试，评估 16 个代表性的分割模型，并展示了几个潜在的应用领域。