现有的基于深度学习（基于DL的）无监督的显着对象检测（USOD）方法基于传统显着性方法和预处理深网的先验知识，在图像中学习显着信息。但是，这些方法采用了一种简单的学习策略来训练深层网络，因此无法将培训样本的“隐藏”信息正确地纳入学习过程。此外，对于分割对象至关重要的外观信息仅在网络训练过程后用作后处理。为了解决这两个问题，我们提出了一个新颖的外观引导的细心自进度学习框架，以无视显着对象检测。提出的框架将自定进度的学习（SPL）和外观指导集成到统一的学习框架中。具体而言，对于第一期，我们提出了一个细心的自进度学习（ASPL）范式，该范式以有意义的命令组织培训样本，以逐步挖掘更详细的显着性信息。我们的ASPL促进了我们的框架，能够自动产生软关注权重，以纯粹的自学方式衡量训练样本的学习难度。对于第二期，我们提出了一个外观指南模块（AGM），该模块将每个像素作为显着性边界的概率的局部外观对比，并通过最大化概率找到目标对象的潜在边界。此外，我们通过汇总其他模态数据的外观向量，例如深度图，热图像或光流，将框架进一步扩展到其他多模式SOD任务。关于RGB，RGB-D，RGB-T和视频SOD基准的广泛实验证明，我们的框架可以针对现有的USOD方法实现最新性能，并且与最新的监督SOD方法相当。

尖峰神经网络是低功率环境的有效计算模型。基于SPIKE的BP算法和ANN-TO-SNN（ANN2SNN）转换是SNN培训的成功技术。然而，尖峰碱BP训练速度很慢，需要大量的记忆成本。尽管Ann2NN提供了一种培训SNN的低成本方式，但它需要许多推理步骤才能模仿训练有素的ANN以表现良好。在本文中，我们提出了一个snn-to-ang（SNN2ANN）框架，以快速和记忆的方式训练SNN。 SNN2ANN由2个组成部分组成：a）ANN和SNN和B）尖峰映射单元之间的重量共享体系结构。首先，该体系结构在ANN分支上训练重量共享参数，从而快速训练和SNN的记忆成本较低。其次，尖峰映射单元确保ANN的激活值是尖峰特征。结果，可以通过训练ANN分支来优化SNN的分类误差。此外，我们设计了一种自适应阈值调整（ATA）算法来解决嘈杂的尖峰问题。实验结果表明，我们的基于SNN2ANN的模型在基准数据集（CIFAR10，CIFAR100和TININE-IMAGENET）上表现良好。此外，SNN2ANN可以在0.625倍的时间步长，0.377倍训练时间，0.27倍GPU内存成本以及基于SPIKE的BP模型的0.33倍尖峰活动下实现可比精度。

无监督的突出物体检测（USOD）对于工业应用和下游任务来说是最重要的意义。基于深度学习（DL）的USOD方法利用多种传统的SOD方法提取的一些低质量的显着性预测，作为显着性提示，主要捕获图像中的一些显着区域。此外，它们通过语义信息的助手优化这些显着性提示，该显着性提示是由其他相关视觉任务中的监督学习训练的一些型号获得的。在这项工作中，我们提出了一种两级激活 - 到显着性（A2S）框架，有效地产生了高质量的显着性提示，并使用这些提示培训强大的耐药性检测器。更重要的是，在整个培训过程中没有人类注释参与我们的框架。在第一阶段中，我们将普雷托网络（MOCO V2）转换为将多级别特征聚合到单个激活图，其中提出了一种自适应决策边界（ADB）来帮助训练变换网络。为了便于生成高质量的伪标签，我们提出了一种损失功能来扩大像素之间的特征距离及其手段。在第二阶段，在线标签纠正（OLR）策略在培训过程中更新伪标签，以减少分散的人的负面影响。此外，我们使用两个残余注意模块（RAM）来构造轻量级显着探测器，其使用低级功能中的互补信息，例如边缘和颜色，从而优化高级功能。对几个SOD基准的广泛实验证明，与现有的USOD方法相比，我们的框架报告了显着性能。此外，在3000张图像上培训我们的框架约1小时，比以前的最先进的方法快30倍。

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.

Gaze estimation is the fundamental basis for many visual tasks. Yet, the high cost of acquiring gaze datasets with 3D annotations hinders the optimization and application of gaze estimation models. In this work, we propose a novel Head-Eye redirection parametric model based on Neural Radiance Field, which allows dense gaze data generation with view consistency and accurate gaze direction. Moreover, our head-eye redirection parametric model can decouple the face and eyes for separate neural rendering, so it can achieve the purpose of separately controlling the attributes of the face, identity, illumination, and eye gaze direction. Thus diverse 3D-aware gaze datasets could be obtained by manipulating the latent code belonging to different face attributions in an unsupervised manner. Extensive experiments on several benchmarks demonstrate the effectiveness of our method in domain generalization and domain adaptation for gaze estimation tasks.

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.