深层自然语言处理（NLP）模型的快速发展导致迫切需要对这些模型单独提出的统一理解。由于缺乏解释低级（例如单词）和高级（例如，短语）特征的统一措施，现有方法无法满足一个框架中不同模型的需求。我们已经开发了一个视觉分析工具DeepNLPVI，以使对文本分类的NLP模型有统一的理解。关键思想是一种基于信息的度量，它提供了有关模型的每一层如何维护样本中输入单词信息的定量解释。我们在每个层的内部和界面信息中对单词对最终预测的重要性以及单词之间的关系（例如短语的形成）进行建模。多层可视化由语料库级，样本级别和单词级可视化组成，支持从整体训练集到单个样本的分析。关于分类任务和模型比较的两个案例研究表明，DeepNLPVI可以帮助用户有效地确定样本和模型架构引起的潜在问题，然后进行明智的改进。

基础学习者和集合中的样本（镜头）几乎没有弹出分类器极大地影响了模型性能。当表现不满意时，通常很难理解基本原因并进行改进。为了解决这个问题，我们提出了一种视觉分析方法FSLDIAGNOTOR。考虑到一组基础学习者和一系列射击的样本，我们考虑了两个问题：1）找到一个很好的基础学习者，可以很好地预测样本集； 2）用更多代表性的镜头代替低质量的镜头，以充分代表样品集。我们将两个问题提出为稀疏子集选择，并开发两种选择算法，分别推荐适当的学习者和射击。将矩阵可视化和散点图组合在一起，以解释上下文中推荐的学习者和镜头，并促进用户调整它们。根据调整，该算法更新了建议结果，以进行另一轮改进。进行了两项案例研究，以证明FSLDIAGNOTOR有助于有效地构建一些分类器，并分别将精度提高12％和21％。

Bokeh效果是一种自然浅的景观现象，使焦点部分陷入摄影。为了追求美学上令人愉悦的照片，人们通常认为散景效应是照片不可或缺的一部分。由于其自然的优势和普遍性，以及许多视觉识别任务的事实可能已经受到“天然散景”现象的负面影响，在这项工作中，我们系统地研究了从新角度，即对抗性散景的散景效应攻击（Advbokeh）旨在将计算的欺骗性信息嵌入到Bokeh生成中，并产生自然的对抗性示例而没有任何人明显的噪声伪影。为此，我们首先提出了一种深度引导的Bokeh合成网络（Debsnet），其能够灵活地合成，重新分析和调整图像的散景水平，具有一级训练程序。 Debsnet允许我们利用Bokeh生成过程并攻击基于后续视觉任务生成现实Bokeh（即，对接地调整深度映射）所需的深度图。为了进一步提高对抗散景的真实性，我们提出了深度引导的梯度基攻击来规范梯度。我们在流行的对手图像分类数据集中验证所提出的方法，即Neurips-2017开发，并表明所提出的方法可以通过高成功率和高图像质量来穿透四个最先进的（SOTA）图像分类网络，即Reset50，VGG，DenSenet和MobileNetv2。通过Advbokeh获得的对抗实例也在黑匣子环境下表现出高水平的可转移性。此外，来自AdvboKeh的离前事实产生的散焦模糊图像实际上可以大写以增强SOTA Defocus Deblurring系统的性能，即IFAN。

本地化隐式功能的最新进展使神经隐式表示能够可扩展到大型场景。然而，这些方法采用的3D空间的定期细分未能考虑到表面占用的稀疏性和几何细节的变化粒度。结果，其内存占地面积与输入体积均别较大，即使在适度密集的分解中也导致禁止的计算成本。在这项工作中，我们为3D表面，编码OCTFIELD提供了一种学习的分层隐式表示，允许具有低内存和计算预算的复杂曲面的高精度编码。我们方法的关键是仅在感兴趣的表面周围分发本地隐式功能的3D场景的自适应分解。我们通过引入分层Octree结构来实现这一目标，以根据表面占用和部件几何形状的丰富度自适应地细分3D空间。随着八十六是离散和不可分辨性的，我们进一步提出了一种新颖的等级网络，其模拟八偏细胞的细分作为概率的过程，并以可差的方式递归地编码和解码八叠结构和表面几何形状。我们展示了Octfield的一系列形状建模和重建任务的价值，显示出在替代方法方面的优越性。

目前的高保真发电和高精度检测DeepFake图像位于臂赛中。我们认为，生产高度逼真和“检测逃避”的深度可以服务于改善未来一代深度检测能力的最终目标。在本文中，我们提出了一种简单但强大的管道，以通过执行隐式空间域陷波滤波来减少假图像的伪影图案而不会损伤图像质量。我们首先表明频域陷波滤波，尽管由于陷波滤波器所需的手动设计，我们的任务对于我们的任务是有效的，但是频域陷波过滤虽然是有效的。因此，我们诉诸基于学习的方法来重现陷波滤波效果，而是仅在空间域中。我们采用添加压倒性的空间噪声来打破周期性噪声模式和深映像滤波来重建无噪声假图像，我们将我们的方法命名为Deadnotch。深度图像过滤为嘈杂图像中的每个像素提供专用过滤器，与其DeepFake对应物相比，产生具有高保真度的滤波图像。此外，我们还使用图像的语义信息来生成对抗性引导映射，以智能地添加噪声。我们对3种代表性的最先进的深蓝进行的大规模评估（在16种DeepFakes上测试）已经证明，我们的技术显着降低了这3种假图像检测方法的准确性，平均和高度为36.79％ 97.02％在最好的情况下。

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a well-labeled source domain to a different but related unlabeled target domain with identical label space. Currently, the main workhorse for solving UDA is domain alignment, which has proven successful. However, it is often difficult to find an appropriate source domain with identical label space. A more practical scenario is so-called partial domain adaptation (PDA) in which the source label set or space subsumes the target one. Unfortunately, in PDA, due to the existence of the irrelevant categories in the source domain, it is quite hard to obtain a perfect alignment, thus resulting in mode collapse and negative transfer. Although several efforts have been made by down-weighting the irrelevant source categories, the strategies used tend to be burdensome and risky since exactly which irrelevant categories are unknown. These challenges motivate us to find a relatively simpler alternative to solve PDA. To achieve this, we first provide a thorough theoretical analysis, which illustrates that the target risk is bounded by both model smoothness and between-domain discrepancy. Considering the difficulty of perfect alignment in solving PDA, we turn to focus on the model smoothness while discard the riskier domain alignment to enhance the adaptability of the model. Specifically, we instantiate the model smoothness as a quite simple intra-domain structure preserving (IDSP). To our best knowledge, this is the first naive attempt to address the PDA without domain alignment. Finally, our empirical results on multiple benchmark datasets demonstrate that IDSP is not only superior to the PDA SOTAs by a significant margin on some benchmarks (e.g., +10% on Cl->Rw and +8% on Ar->Rw ), but also complementary to domain alignment in the standard UDA

Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation. By inverting such renderer, one can think of a learning approach to infer 3D information from 2D images. However, standard graphics renderers involve a fundamental discretization step called rasterization, which prevents the rendering process to be differentiable, hence able to be learned. Unlike the state-of-the-art differentiable renderers [29,19], which only approximate the rendering gradient in the back propagation, we propose a truly differentiable rendering framework that is able to (1) directly render colorized mesh using differentiable functions and (2) back-propagate efficient supervision signals to mesh vertices and their attributes from various forms of image representations, including silhouette, shading and color images. The key to our framework is a novel formulation that views rendering as an aggregation function that fuses the probabilistic contributions of all mesh triangles with respect to the rendered pixels. Such formulation enables our framework to flow gradients to the occluded and far-range vertices, which cannot be achieved by the previous state-of-thearts. We show that by using the proposed renderer, one can achieve significant improvement in 3D unsupervised singleview reconstruction both qualitatively and quantitatively. Experiments also demonstrate that our approach is able to handle the challenging tasks in image-based shape fitting, which remain nontrivial to existing differentiable renderers. Code is available at https://github.com/ ShichenLiu/SoftRas.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.