Camouflaged object detection is a challenging task that aims to identify
objects that are highly similar to their background. Due to the powerful
noise-to-image denoising capability of denoising diffusion models, in this
paper, we propose a diffusion-based framework for camouflaged object detection,
termed diffCOD, a new framework that considers the camouflaged object
segmentation task as a denoising diffusion process from noisy masks to object
masks. Specifically, the object mask diffuses from the ground-truth masks to a
random distribution, and the designed model learns to reverse this noising
process. To strengthen the denoising learning, the input image prior is encoded
and integrated into the denoising diffusion model to guide the diffusion
process. Furthermore, we design an injection attention module (IAM) to interact
conditional semantic features extracted from the image with the diffusion noise
embedding via the cross-attention mechanism to enhance denoising learning.
Extensive experiments on four widely used COD benchmark datasets demonstrate
that the proposed method achieves favorable performance compared to the
existing 11 state-of-the-art methods, especially in the detailed texture
segmentation of camouflaged objects. Our code will be made publicly available
at: this https URL

本研究提出了一种基于扩散的伪装目标检测框架（diffCOD），该框架将伪装目标分割任务视为从噪声掩码到目标掩码的去噪扩散过程，并采用模型学习来逆转这一噪声处理过程。通过将输入图像先验编码和集成到去噪扩散模型中来加强去噪学习，同时使用注入注意力模块（IAM）通过交叉注意机制将从图像中提取的条件语义特征与扩散噪声嵌入相互作用以增强去噪学习。实验结果表明，在四个广泛使用的伪装目标检测基准数据集上，该方法相较于其他 11 种最先进的方法，尤其在伪装目标的纹理细分割上取得了有利的性能。

伪装目标检测的扩散模型

Diffusion Model for Camouflaged Object Detection

Determining the drivable area, or free space segmentation, is critical for
mobile robots to navigate indoor environments safely. However, the lack of
coherent markings and structures (e.g., lanes, curbs, etc.) in indoor spaces
places the burden of traversability estimation heavily on the mobile robot.
This paper explores the use of a self-supervised one-shot texture segmentation
framework and an RGB-D camera to achieve robust drivable area segmentation.
With a fast inference speed and compact size, the developed model, MOSTS is
ideal for real-time robot navigation and various embedded applications. A
benchmark study was conducted to compare MOSTS's performance with existing
one-shot texture segmentation models to evaluate its performance. Additionally,
a validation dataset was built to assess MOSTS's ability to perform texture
segmentation in the wild, where it effectively identified small low-lying
objects that were previously undetectable by depth measurements. Further, the
study also compared MOSTS's performance with two State-Of-The-Art (SOTA) indoor
semantic segmentation models, both quantitatively and qualitatively. The
results showed that MOSTS offers comparable accuracy with up to eight times
faster inference speed in indoor drivable area segmentation.

本文研究利用自监督一次性纹理分割框架和 RGB-D 相机实现鲁棒的可行驶区域分割并在与现有一次性纹理分割模型和两种最先进的室内语义分割模型进行比较的基准研究中表现出极具竞争力的准确性和速度。

一种自监督微型一次性纹理分割 (MOSTS) 模型，用于实时机器人导航和嵌入式应用

A Self-Supervised Miniature One-Shot Texture Segmentation (MOSTS) Model  for Real-Time Robot Navigation and Embedded Applications

The UNet model consists of fully convolutional network (FCN) layers arranged
as contracting encoder and upsampling decoder maps. Nested arrangements of
these encoder and decoder maps give rise to extensions of the UNet model, such
as UNete and UNet++. Other refinements include constraining the outputs of the
convolutional layers to discriminate between segment labels when trained end to
end, a property called deep supervision. This reduces feature diversity in
these nested UNet models despite their large parameter space. Furthermore, for
texture segmentation, pixel correlations at multiple scales contribute to the
classification task; hence, explicit deep supervision of shallower layers is
likely to enhance performance. In this paper, we propose ADS UNet, a stage-wise
additive training algorithm that incorporates resource-efficient deep
supervision in shallower layers and takes performance-weighted combinations of
the sub-UNets to create the segmentation model. We provide empirical evidence
on three histopathology datasets to support the claim that the proposed ADS
UNet reduces correlations between constituent features and improves performance
while being more resource efficient. We demonstrate that ADS_UNet outperforms
state-of-the-art Transformer-based models by 1.08 and 0.6 points on CRAG and
BCSS datasets, and yet requires only 37% of GPU consumption and 34% of training
time as that required by Transformers.

本文提出了 ADS UNet 算法，通过阶段性的增量式训练实现对浅层网络的高效深度监督，并对子 UNet 模型进行性能加权组合，以降低特征之间的相关性并提高分割性能，成功地在三个组织切片数据集中超越基于 Transformer 的最先进模型，而且只需要 37% 的 GPU 资源和 34% 的训练时间。