In the battle against widespread online misinformation, a growing problem is
text-image inconsistency, where images are misleadingly paired with texts with
different intent or meaning. Existing classification-based methods for
text-image inconsistency can identify contextual inconsistencies but fail to
provide explainable justifications for their decisions that humans can
understand. Although more nuanced, human evaluation is impractical at scale and
susceptible to errors. To address these limitations, this study introduces
D-TIIL (Diffusion-based Text-Image Inconsistency Localization), which employs
text-to-image diffusion models to localize semantic inconsistencies in text and
image pairs. These models, trained on large-scale datasets act as ``omniscient"
agents that filter out irrelevant information and incorporate background
knowledge to identify inconsistencies. In addition, D-TIIL uses text embeddings
and modified image regions to visualize these inconsistencies. To evaluate
D-TIIL's efficacy, we introduce a new TIIL dataset containing 14K consistent
and inconsistent text-image pairs. Unlike existing datasets, TIIL enables
assessment at the level of individual words and image regions and is carefully
designed to represent various inconsistencies. D-TIIL offers a scalable and
evidence-based approach to identifying and localizing text-image inconsistency,
providing a robust framework for future research combating misinformation.

为了解决在线误导信息的问题，该研究引入了 D-TIIL（基于扩散的文本 - 图像不一致性定位），采用文本对图像扩散模型来定位文本和图像对中的语义不一致性，并通过文本嵌入和修改后的图像区域进行可视化。

使用扩散模型揭示文本 - 图像不一致性

Exposing Text-Image Inconsistency Using Diffusion Models

Depth completion is a long-standing challenge in computer vision, where
classification-based methods have made tremendous progress in recent years.
However, most existing classification-based methods rely on pre-defined
pixel-shared and discrete depth values as depth categories. This representation
fails to capture the continuous depth values that conform to the real depth
distribution, leading to depth smearing in boundary regions. To address this
issue, we revisit depth completion from the clustering perspective and propose
a novel clustering-based framework called CluDe which focuses on learning the
pixel-wise and continuous depth representation. The key idea of CluDe is to
iteratively update the pixel-shared and discrete depth representation to its
corresponding pixel-wise and continuous counterpart, driven by the real depth
distribution. Specifically, CluDe first utilizes depth value clustering to
learn a set of depth centers as the depth representation. While these depth
centers are pixel-shared and discrete, they are more in line with the real
depth distribution compared to pre-defined depth categories. Then, CluDe
estimates offsets for these depth centers, enabling their dynamic adjustment
along the depth axis of the depth distribution to generate the pixel-wise and
continuous depth representation. Extensive experiments demonstrate that CluDe
successfully reduces depth smearing around object boundaries by utilizing
pixel-wise and continuous depth representation. Furthermore, CluDe achieves
state-of-the-art performance on the VOID datasets and outperforms
classification-based methods on the KITTI dataset.

深度完整性的一个长期挑战是计算机视觉领域中的一个难题。近年来，基于分类的方法取得了巨大的进展。然而，大多数现有的基于分类的方法依赖于预定义的像素共享和离散深度值作为深度类别。本文从聚类的角度重新思考深度完整性，并提出了一种名为 CluDe 的新型基于聚类的框架，它专注于学习像素级和连续深度表示。CluDe 的关键思想是利用像素值的聚类来学习一组深度中心作为深度表示。然后，CluDe 对这些深度中心估计偏移量，使其可以在深度分布的深度轴上进行动态调整，从而生成像素级和连续深度表示。广泛的实验表明，CluDe 成功地利用像素级和连续深度表示降低了对象边界处的深度模糊。此外，CluDe 在 VOID 数据集上实现了最先进的性能，并在 KITTI 数据集上超过了基于分类的方法。

通过聚类学习像素级连续深度表示以用于深度完整

Learning Pixel-wise Continuous Depth Representation via Clustering for  Depth Completion

Anomaly detection is defined as the problem of finding data points that do
not follow the patterns of the majority. Among the various proposed methods for
solving this problem, classification-based methods, including one-class Support
Vector Machines (SVM) are considered effective and state-of-the-art. The
one-class SVM method aims to find a decision boundary to distinguish between
normal data points and anomalies using only the normal data. On the other hand,
most real-world problems involve some degree of uncertainty, where the true
probability distribution of each data point is unknown, and estimating it is
often difficult and costly. Assuming partial distribution information such as
the first and second-order moments is known, a distributionally robust
chance-constrained model is proposed in which the probability of
misclassification is low. By utilizing a mapping function to a higher
dimensional space, the proposed model will be capable of classifying
origin-inseparable datasets. Also, by adopting the kernel idea, the need for
explicitly knowing the mapping is eliminated, computations can be performed in
the input space, and computational complexity is reduced. Computational results
validate the robustness of the proposed model under different probability
distributions and also the superiority of the proposed model compared to the
standard one-class SVM in terms of various evaluation metrics.

基于分类的方法在异常检测中十分有效，本论文提出了一种分布稳健的概率约束模型，利用核函数的方法来分类难以分离的数据集，并且在验证中显示了该模型的鲁棒性和优越性。

使用分布鲁棒优化方法进行不确定性下的异常检测

Anomaly Detection Under Uncertainty Using Distributionally Robust  Optimization Approach

Anomaly detection, finding patterns that substantially deviate from those
seen previously, is one of the fundamental problems of artificial intelligence.
Recently, classification-based methods were shown to achieve superior results
on this task. In this work, we present a unifying view and propose an open-set
method, GOAD, to relax current generalization assumptions. Furthermore, we
extend the applicability of transformation-based methods to non-image data
using random affine transformations. Our method is shown to obtain
state-of-the-art accuracy and is applicable to broad data types. The strong
performance of our method is extensively validated on multiple datasets from
different domains.

本文提出了一种基于分类的方法 GOAD 来检测异常数据，同时拓展了基于变换方法的适用性，并且在多个数据集上得到了最新的准确性表现。