The efficient segmentation of foreground text information from the background in degraded color document images is a hot research topic. Due to the imperfect preservation of ancient documents over a long period of time, various types of degradation, including staining, yellowing, and ink seepage, have seriously affected the results of image binarization. In this paper, a three-stage method is proposed for image enhancement and binarization of degraded color document images by using discrete wavelet transform (DWT) and generative adversarial network (GAN). In Stage-1, we use DWT and retain the LL subband images to achieve the image enhancement. In Stage-2, the original input image is split into four (Red, Green, Blue and Gray) single-channel images, each of which trains the independent adversarial networks. The trained adversarial network models are used to extract the color foreground information from the images. In Stage-3, in order to combine global and local features, the output image from Stage-2 and the original input image are used to train the independent adversarial networks for document binarization. The experimental results demonstrate that our proposed method outperforms many classical and state-of-the-art (SOTA) methods on the Document Image Binarization Contest (DIBCO) dataset. We release our implementation code at https://github.com/abcpp12383/ThreeStageBinarization.

我们提出了一种神经网络体系结构，用于糖尿病足溃疡和结肠镜检查息肉的医学图像分割。糖尿病足溃疡是由糖尿病的神经性和血管并发症引起的。为了提供适当的诊断和治疗，伤口护理专业人员需要从脚伤中提取准确的形态特征。使用计算机辅助系统是一种提取相关形态特征并分割病变的有前途的方法。我们提出了一个称为HardNet-DFU的卷积神经网络，通过增强主链并取代HardNet-MSEG的解码器，该网络是2021年的结肠镜检查息肉分割的SOTA。 DFU使用DFUC2022数据集并通过五倍的交叉验证，测试时间扩展等增加其稳健性。在DFUC2022的验证阶段，HardNet-DFUS达到0.7063平均骰子，并在所有参与者中排名第三。在DFUC2022的最终测试阶段，它达到了0.7287的平均骰子，并且是第一名。 HardNet-DFU还为结肠镜检查息肉分割任务提供出色的性能。它在著名的kvasir数据集上达到了0.924的平均骰子，比原始硬核MSEG提高了1.2 \％。这些代码可在https://github.com/kytimmylai/dfuc2022（用于糖尿病足溃疡细分）和https://github.com/yuwenlo/hardnet-dfus（用于结肠镜息肉分割）。

通过在计算机视觉（CV）领域深度学习算法的良好性能，卷积神经网络（CNN）体系结构已成为计算机视觉任务的主要骨干。随着移动设备的广泛使用，基于计算能力低的平台的神经网络模型逐渐引起人们的注意。但是，由于计算能力的限制，移动设备上通常无法使用深度学习算法。本文提出了一个轻巧的卷积神经网络TripLenet，可以在Raspberry Pi上轻松运行。从阈值中的块连接概念中采用，新提出的网络模型会压缩并加速网络模型，减少网络的参数量，并在确保准确性的同时缩短每个图像的推理时间。我们提出的TripLenet和其他最先进的（SOTA）神经网络在Raspberry Pi上使用CIFAR-10和SVHN数据集进行了图像分类实验。实验结果表明，与GhostNet，Mobilenet，Theashnet，EdefityNet和HardNet相比，每图像的推理时间分别缩短了15％，16％，17％，24％和30％。

随着计算机愿景任务中的神经网络的不断发展，越来越多的网络架构取得了突出的成功。作为最先进的神经网络架构之一，DenSenet捷径所有特征映射都可以解决模型深度的问题。虽然这种网络架构在低MAC（乘法和累积）上具有优异的准确性，但它需要过度推理时间。为了解决这个问题，HardNet减少了特征映射之间的连接，使得其余连接类似于谐波。然而，这种压缩方法可能导致模型精度和增加的MAC和模型大小降低。该网络架构仅减少了内存访问时间，需要改进其整体性能。因此，我们提出了一种新的网络架构，使用阈值机制来进一步优化连接方法。丢弃不同卷积层的不同数量的连接以压缩阈值中的特征映射。所提出的网络架构使用了三个数据集，CiFar-10，CiFar-100和SVHN，以评估图像分类的性能。实验结果表明，与DENSENET相比，阈值可降低推理时间高达60％，并且在这些数据集上的硬盘相比，训练速度快高达35％的训练速度和20％的误差率降低。

深度神经网络在计算机视野领域取得了重大进展。最近的研究表明，神经网络架构的深度，宽度和快捷方式连接在其性能中起着至关重要的作用。最先进的神经网络架构DenSenet之一，通过密集连接实现了优异的收敛速率。但是，它仍然具有明显的缺点在内存量的使用情况。在本文中，我们介绍了一种新型的修剪工具，阈值，这是指MOSFET中阈值电压的原理。这项工作采用此方法以不同的方式连接不同深度的块以减少内存的使用情况。它表示为阈值。我们在CiFar10的数据集上评估阈值和其他不同网络。实验表明，HardNet是DenSenet的两倍，在此基础上，阈值比HardNet更快10％，误差率降低10％。

Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. However, domain discrepancies in low-level image statistics and high-level contexts compromise the segmentation performance over the target domain. A key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. Unfortunately, there is a lack of such unified approaches for UDA tasks in the existing literature. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation. Concretely, for image-level domain shifts, we propose a global photometric alignment module and a global texture alignment module that align images in the source and target domains in terms of image-level properties. For feature-level domain shifts, we perform global manifold alignment by projecting pixel features from both domains onto the feature manifold of the source domain; and we further regularize category centers in the source domain through a category-oriented triplet loss and perform target domain consistency regularization over augmented target domain images. Experimental results demonstrate that our pipeline significantly outperforms previous methods. In the commonly tested GTA5$\rightarrow$Cityscapes task, our proposed method using Deeplab V3+ as the backbone surpasses previous SOTA by 8%, achieving 58.2% in mIoU.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities. Prior methods successfully preserve the character of clothing images, however, occlusion remains a pernicious effect for realistic virtual try-on. In this work, we first present a comprehensive analysis of the occlusions and categorize them into two aspects: i) Inherent-Occlusion: the ghost of the former cloth still exists in the try-on image; ii) Acquired-Occlusion: the target cloth warps to the unreasonable body part. Based on the in-depth analysis, we find that the occlusions can be simulated by a novel semantically-guided mixup module, which can generate semantic-specific occluded images that work together with the try-on images to facilitate training a de-occlusion try-on (DOC-VTON) framework. Specifically, DOC-VTON first conducts a sharpened semantic parsing on the try-on person. Aided by semantics guidance and pose prior, various complexities of texture are selectively blending with human parts in a copy-and-paste manner. Then, the Generative Module (GM) is utilized to take charge of synthesizing the final try-on image and learning to de-occlusion jointly. In comparison to the state-of-the-art methods, DOC-VTON achieves better perceptual quality by reducing occlusion effects.

Panoptic Part Segmentation (PPS) unifies panoptic segmentation and part segmentation into one task. Previous works utilize separated approaches to handle thing, stuff, and part predictions without shared computation and task association. We aim to unify these tasks at the architectural level, designing the first end-to-end unified framework named Panoptic-PartFormer. Moreover, we find the previous metric PartPQ biases to PQ. To handle both issues, we make the following contributions: Firstly, we design a meta-architecture that decouples part feature and things/stuff feature, respectively. We model things, stuff, and parts as object queries and directly learn to optimize all three forms of prediction as a unified mask prediction and classification problem. We term our model as Panoptic-PartFormer. Secondly, we propose a new metric Part-Whole Quality (PWQ) to better measure such task from both pixel-region and part-whole perspectives. It can also decouple the error for part segmentation and panoptic segmentation. Thirdly, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross attention scheme to further boost part segmentation qualities. We design a new part-whole interaction method using masked cross attention. Finally, the extensive ablation studies and analysis demonstrate the effectiveness of both Panoptic-PartFormer and Panoptic-PartFormer++. Compared with previous Panoptic-PartFormer, our Panoptic-PartFormer++ achieves 2% PartPQ and 3% PWQ improvements on the Cityscapes PPS dataset and 5% PartPQ on the Pascal Context PPS dataset. On both datasets, Panoptic-PartFormer++ achieves new state-of-the-art results with a significant cost drop of 70% on GFlops and 50% on parameters. Our models can serve as a strong baseline and aid future research in PPS. Code will be available.

In recent years, the Transformer architecture has shown its superiority in the video-based person re-identification task. Inspired by video representation learning, these methods mainly focus on designing modules to extract informative spatial and temporal features. However, they are still limited in extracting local attributes and global identity information, which are critical for the person re-identification task. In this paper, we propose a novel Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel designed proxy embedding modules to address the above issue. Specifically, MSTAT consists of three stages to encode the attribute-associated, the identity-associated, and the attribute-identity-associated information from the video clips, respectively, achieving the holistic perception of the input person. We combine the outputs of all the stages for the final identification. In practice, to save the computational cost, the Spatial-Temporal Aggregation (STA) modules are first adopted in each stage to conduct the self-attention operations along the spatial and temporal dimensions separately. We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages. All of them are realized by employing newly designed self-attention operations with specific meanings. Moreover, temporal patch shuffling is also introduced to further improve the robustness of the model. Extensive experimental results demonstrate the effectiveness of the proposed modules in extracting the informative and discriminative information from the videos, and illustrate the MSTAT can achieve state-of-the-art accuracies on various standard benchmarks.