光流是一种易于构思和珍贵的提示，用于推进无监督的视频对象细分（UVOS）。以前的大多数方法直接提取并融合了在UVOS设置中分割目标对象的运动和外观特征。但是，光流本质上是连续帧之间所有像素的瞬时速度，因此使运动特征与相应帧之间的主要对象不太对齐。为了解决上述挑战，我们为外观和运动特征对齐方式提出了一个简洁，实用和有效的体系结构，称为层次特征对齐网络（HFAN）。具体而言，HFAN中的关键优点是顺序特征对齐（FAM）模块和特征适应（FAT）模块，这些模块被利用用于处理外观和运动特征。 FAM能够分别将外观和运动特征与主要对象语义表示分别对齐。此外，脂肪是针对外观和运动特征的自适应融合而显式设计的，以实现跨模式特征之间的理想权衡。广泛的实验证明了拟议的HFAN的有效性，该实验在Davis-16上达到了新的最新性能，达到88.7 $ \ MATHCAL {J} \＆\ MATHCAL {F} $，即相对改进，即相对改进比最佳发布结果比3.5％。

仅使用图像级标签的弱监督语义细分旨在降低分割任务的注释成本。现有方法通常利用类激活图（CAM）来定位伪标签生成的对象区域。但是，凸轮只能发现对象的最歧视部分，从而导致下像素级伪标签。为了解决这个问题，我们提出了一个限制的显着性和内类关系的显着性（I $^2 $ CRC）框架，以协助CAM中激活的对象区域的扩展。具体而言，我们提出了一个显着性指导的类不足的距离模块，以通过将特征对准其类原型来更接近类别内特征。此外，我们提出了一个特定的距离模块，以将类间特征推开，并鼓励对象区域的激活高于背景。除了加强分类网络激活CAM中更多积分对象区域的能力外，我们还引入了一个对象引导的标签细化模块，以完全利用分割预测和初始标签，以获取出色的伪标签。 Pascal VOC 2012和可可数据集的广泛实验很好地证明了I $^2 $ CRC的有效性，而不是其他最先进的对应物。源代码，模型和数据已在\ url {https://github.com/nust-machine-intelligence-laboratory/i2crc}提供。

零射击学习（ZSL）通过将语义知识转移到看不见者的语义知识来解决新的类识别问题。通过单独使用单向关注，现有的基于关注的模型在单个图像中努力学习劣势区域特征，这忽略了视觉特征的可转换性和辨别属性定位。在本文中，我们提出了一个跨属性引导的变换器网络，称为Transzero ++，以改进可视化功能，并学习精确的属性本地化，用于ZSL中的语义增强的可视嵌入表示。 Transzero ++由Attribute $ \ LightArrow $ Visual Transformer子网（AVT）和Visual $ \ LightArrow $属性变压器子网（增值税）组成。具体而言，AVT首先采用功能增强编码器来缓解交叉数据集问题，并通过减少区域特征之间的缠绕的相对几何关系来提高视觉特征的可转换性。然后，使用属性$ \ lightArrow $可视解码器来本地化与基于属性的可视特征表示的给定图像中的每个属性最相关的图像区域。类似地，VAT使用类似的功能增强编码器来改进视觉功能，这些功能进一步应用于Visual $ \ lightarrow $属性解码器，以学习基于Visual-基的属性功能。通过进一步引入语义协作损失，两个属性引导的变压器通过语义协作学习互相教导学习语义增强的视觉嵌入。广泛的实验表明，Transzero ++在三个挑战ZSL基准上实现了新的最先进的结果。该代码可用于：\ url {https://github.com/shiming-chen/transzero_pp}。

零射门学习（ZSL）旨在通过将语义知识从看见课程转移到看不见者来识别新颖的课程。从不同类别之间共享的属性描述中学到的语义知识，该属性描述是用于本地化代表歧视区域特征的对象属性的强子指数，从而实现了显着的视觉语义交互。尽管基于注意的模型已经尝试学习单个图像中的这种区域特征，但是通常忽略视觉特征的可转换性和辨别性属性定位。在本文中，我们提出了一个属性引导的变压器网络，称为Transzero，以改进视觉特征，并在ZSL中鉴定鉴别的视觉嵌入表示。具体而言，Transzero采用特征增强编码器来缓解想象集和ZSL基准之间的交叉数据集偏压，并通过减少区域特征之间的缠结的相对几何关系来提高视觉特征的可转换性。为了学习地区增强的可视功能，Transzero使用视觉语义解码器来在语义属性信息的指导下本地化与给定图像中的每个属性最相关的图像区域。然后，用于在视觉语义嵌入网络中进行有效的视觉语义交互来实现局部增强的视觉特征和语义向量。广泛的实验表明，Transzero在三个ZSL基准上实现了新的最新状态。该代码可用于：\ url {https://github.com/shiming-chen/transzero}。

动态纹理（DT）在时间维度中表现出空间结构域和随机重复性的统计平稳性，表明DT的不同帧具有高度相似性相关性，这是关键的先验知识。但是，现有方法无法有效地从少数培训数据中学习有希望的高维DT合成模型。在本文中，我们提出了一种新颖的DT合成方法，该方法充分利用了先验知识来解决此问题。我们的方法基于提出的内核相似性嵌入，这不仅可以减轻高维度和小样本问题，而且还具有建模非线性特征关系的优势。具体而言，我们首先提出了两个假设，这些假设对于DT模型使用相似性相关性生成新框架至关重要。然后，我们将内核学习和极端学习机集成到统一的合成模型中，以学习代表DT的内核相似性。从Internet和两个基准数据集（即Gatech GraphCut纹理和Dyntex）收集的DT视频的广泛实验表明，学到的内核相似性嵌入可以有效地显示出DT的歧视性表示。因此，我们的方法能够保留具有出色的可持续性和概括的合成DT序列的长期时间连续性。同时，与最先进的方法相比，它有效地生成了具有快速和低计算的现实DT视频。代码和更多综合视频可在我们的项目页面https://shiming-chen.github.io/similarity-page/similarit.html上获得。

Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt, have demonstrated strong performance in various scenarios. While these models were originally designed for supervised learning with ImageNet labels, they can also potentially benefit from self-supervised learning techniques such as masked autoencoders (MAE). However, we found that simply combining these two approaches leads to subpar performance. In this paper, we propose a fully convolutional masked autoencoder framework and a new Global Response Normalization (GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation. We also provide pre-trained ConvNeXt V2 models of various sizes, ranging from an efficient 3.7M-parameter Atto model with 76.7% top-1 accuracy on ImageNet, to a 650M Huge model that achieves a state-of-the-art 88.9% accuracy using only public training data.

A step-search sequential quadratic programming method is proposed for solving nonlinear equality constrained stochastic optimization problems. It is assumed that constraint function values and derivatives are available, but only stochastic approximations of the objective function and its associated derivatives can be computed via inexact probabilistic zeroth- and first-order oracles. Under reasonable assumptions, a high-probability bound on the iteration complexity of the algorithm to approximate first-order stationarity is derived. Numerical results on standard nonlinear optimization test problems illustrate the advantages and limitations of our proposed method.

Masked image modeling (MIM) has shown great promise for self-supervised learning (SSL) yet been criticized for learning inefficiency. We believe the insufficient utilization of training signals should be responsible. To alleviate this issue, we introduce a conceptually simple yet learning-efficient MIM training scheme, termed Disjoint Masking with Joint Distillation (DMJD). For disjoint masking (DM), we sequentially sample multiple masked views per image in a mini-batch with the disjoint regulation to raise the usage of tokens for reconstruction in each image while keeping the masking rate of each view. For joint distillation (JD), we adopt a dual branch architecture to respectively predict invisible (masked) and visible (unmasked) tokens with superior learning targets. Rooting in orthogonal perspectives for training efficiency improvement, DM and JD cooperatively accelerate the training convergence yet not sacrificing the model generalization ability. Concretely, DM can train ViT with half of the effective training epochs (3.7 times less time-consuming) to report competitive performance. With JD, our DMJD clearly improves the linear probing classification accuracy over ConvMAE by 5.8%. On fine-grained downstream tasks like semantic segmentation, object detection, etc., our DMJD also presents superior generalization compared with state-of-the-art SSL methods. The code and model will be made public at https://github.com/mx-mark/DMJD.

Considering the computation complexity, we propose a Guided Hybrid Quantization with One-to-one Self-Teaching (GHOST}) framework. More concretely, we first design a structure called guided quantization self-distillation (GQSD), which is an innovative idea for realizing lightweight through the synergy of quantization and distillation. The training process of the quantization model is guided by its full-precision model, which is time-saving and cost-saving without preparing a huge pre-trained model in advance. Second, we put forward a hybrid quantization (HQ) module to obtain the optimal bit width automatically under a constrained condition where a threshold for distribution distance between the center and samples is applied in the weight value search space. Third, in order to improve information transformation, we propose a one-to-one self-teaching (OST) module to give the student network a ability of self-judgment. A switch control machine (SCM) builds a bridge between the student network and teacher network in the same location to help the teacher to reduce wrong guidance and impart vital knowledge to the student. This distillation method allows a model to learn from itself and gain substantial improvement without any additional supervision. Extensive experiments on a multimodal dataset (VEDAI) and single-modality datasets (DOTA, NWPU, and DIOR) show that object detection based on GHOST outperforms the existing detectors. The tiny parameters (<9.7 MB) and Bit-Operations (BOPs) (<2158 G) compared with any remote sensing-based, lightweight or distillation-based algorithms demonstrate the superiority in the lightweight design domain. Our code and model will be released at https://github.com/icey-zhang/GHOST.

Automatic font generation without human experts is a practical and significant problem, especially for some languages that consist of a large number of characters. Existing methods for font generation are often in supervised learning. They require a large number of paired data, which are labor-intensive and expensive to collect. In contrast, common unsupervised image-to-image translation methods are not applicable to font generation, as they often define style as the set of textures and colors. In this work, we propose a robust deformable generative network for unsupervised font generation (abbreviated as DGFont++). We introduce a feature deformation skip connection (FDSC) to learn local patterns and geometric transformations between fonts. The FDSC predicts pairs of displacement maps and employs the predicted maps to apply deformable convolution to the low-level content feature maps. The outputs of FDSC are fed into a mixer to generate final results. Moreover, we introduce contrastive self-supervised learning to learn a robust style representation for fonts by understanding the similarity and dissimilarities of fonts. To distinguish different styles, we train our model with a multi-task discriminator, which ensures that each style can be discriminated independently. In addition to adversarial loss, another two reconstruction losses are adopted to constrain the domain-invariant characteristics between generated images and content images. Taking advantage of FDSC and the adopted loss functions, our model is able to maintain spatial information and generates high-quality character images in an unsupervised manner. Experiments demonstrate that our model is able to generate character images of higher quality than state-of-the-art methods.