United States

Self-supervised anomaly detection and localization are critical to real-world scenarios in which collecting anomalous samples and pixel-wise labeling is tedious or even infeasible, even worse when a wide variety of unseen anomalies could surface at test time. Our approach involves a pretext task in the context of masked image modeling, where the goal is to impose agreement between cluster assignments obtained from the representation of an image view containing saliency-aware masked patches and the uncorrupted image view. We harness the self-attention map extracted from the transformer to mask non-salient image patches without destroying the crucial structure associated with the foreground object. Subsequently, the pre-trained model is fine-tuned to detect and localize simulated anomalies generated under the guidance of the transformer&#39;s self-attention map. We conducted extensive validation and ablations on the benchmark of industrial images and achieved superior performance against competing methods. We also show the adaptability of our method to the medical images of the chest X-rays benchmark. 

AAAI 2023

Attention-Conditioned Augmentations for Self-Supervised Anomaly Detection and Localization

medical imaging

anomaly detection

self-supervised learning

AIAI 2020 (16th International Conference on Artificial Intelligence Applications and Innovations) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually. Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference.

AIAI 2020

Self-supervised anomaly detection and localization are critical to real-world scenarios in which collecting anomalous samples and pixel-wise labeling is tedious or even infeasible, even worse when a wide variety of unseen anomalies could surface at test time. Our approach involves a pretext task in the context of masked image modeling, where the goal is to impose agreement between cluster assignments obtained from the representation of an image view containing saliency-aware masked patches and the uncorrupted image view. We harness the self-attention map extracted from the transformer to mask non-salient image patches without destroying the crucial structure associated with the foreground object. Subsequently, the pre-trained model is fine-tuned to detect and localize simulated anomalies generated under the guidance of the transformer's self-attention map. We conducted extensive validation and ablations on the benchmark of industrial images and achieved superior performance against competing methods. We also show the adaptability of our method to the medical images of the chest X-rays benchmark. 

poster

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

The performances of defect inspection have been severely hindered by insufficient defect images in industries, which can be alleviated by generating more samples as data augmentation. We propose the first defect image generation method in the challenging few-shot cases. Given just a handful of defect images and relatively more defect-free ones, our goal is to augment the dataset with new defect images. Our method consists of two training stages. First, we train a data-efficient StyleGAN2 on defect-free images as the backbone. Second, we attach defect-aware residual blocks to the backbone, which learn to produce reasonable defect masks and accordingly manipulate the features within the masked regions by training the added modules on limited defect images. Extensive experiments on MVTec AD dataset not only validate the effectiveness of our method in generating realistic and diverse defect images, but also manifest the benefits it brings to downstream defect inspection tasks.

Few-Shot Defect Image Generation via Defect-Aware Feature Manipulation

Solving reachability games is a fundamental problem for the analysis, verification, and synthesis of reactive systems.
We consider logical reachability games modulo theories (in short, GMTs), i.e.,
infinite-state games whose rules are defined by logical formulas over a multi-sorted first-order theory. 
Our games have an asymmetric constraint: the safety player has at most k possible moves from each game configuration, whereas the reachability player has no such limitation.
Even though determining the winner of such a GMT is undecidable, it can be reduced to the well-studied problem of checking the satisfiability of a system of  constrained Horn clauses (CHCs), for which many off-the-shelf solvers have been developed.
Winning strategies for GMTs can also be computed by resorting to suitable CHC queries. 
We demonstrate that GMTs can model various relevant real-world games, and that our approach can effectively solve several problems from different domains, using Z3 as the backend CHC solver.

Reachability Games Modulo Theories with a Bounded Safety Player

Deep learning frameworks optimize the computation graphs and intra-operator computations to boost the inference performance on GPUs,  while inter-operator parallelism is usually ignored.  In this paper, a unified framework, AutoGraph, is proposed to obtain highly optimized computation graphs in favor of parallel executions of GPU kernels.  A novel dynamic programming algorithm, combined with backtracking search, is adopted to explore the optimal graph optimization solution, with the fast performance estimation from the mixed critical path cost. Accurate runtime information based on GPU Multi-Stream launched with CUDA Graph is utilized to determine the convergence of the optimization. Experimental results demonstrate that our method achieves up to 3.47x speedup over existing graph optimization methods. Moreover, AutoGraph outperforms state-of-the-art parallel kernel launch frameworks by up to 1.26x. 

AutoGraph: Optimizing DNN Computation Graph for Parallel GPU Kernel Execution

Designing private voting rules is an important and pressing problem for trustworthy democracy. In this paper, under the framework of differential privacy, we propose a novel famliy of randomized voting rules based on the well-known Condorcet method, and focus on three classes of voting rules in this family: Laplacian Condorcet method ($\CMLAP_\lambda$), exponential Condorcet method ($\CMEXP_\lambda$), and randomized response Condorcet method ($\CMRR_\lambda$), where $\lambda$ represents the level of noise. We prove that all of our rules satisfy absolute monotonicity, lexi-participation, probabilistic Pareto efficiency, approximate probabilistic Condorcet criterion, and approximate SD-strategyproofness. In addition, $\CMRR_\lambda$ satisfies (non-approximate) probabilistic Condorcet criterion, while $\CMLAP_\lambda$ and $\CMEXP_\lambda$ satisfy strong lexi-participation. Finally, we regard differential privacy as a voting axiom, and discuss its relations to other axioms.

Differentially Private Condorcet Voting

Graph Contrastive Learning (GCL) has drawn much research interest due to its strong ability to capture both graph structure and node attribute information in a self-supervised manner. Current GCL methods usually adopt Graph Neural Networks (GNNs) as the base encoder, which typically relies on the homophily assumption of networks and overlooks node similarity in the attribute space. There are many scenarios where such assumption cannot be satisfied, or node similarity plays a crucial role. In order to design a more robust mechanism, we develop a novel attribute and structure preserving graph contrastive learning framework, named ASP, which comprehensively and efficiently preserves node attributes while exploiting graph structure. Specifically, we consider three different graph views in our framework, i.e., original view, attribute view, and global structure view. Then, we perform contrastive learning across three views in a joint fashion, mining comprehensive graph information. We validate the effectiveness of the proposed framework on various real-world networks with different levels of homophily. The results demonstrate the superior performance of our model over the representative baselines.

Attribute and Structure Preserving Graph Contrastive Learning

In the wake of increasing political extremism, online platforms have been criticized for contributing to polarization. One line of criticism has focused on echo chambers and the recommended content served to users by these platforms. In this work, we introduce the fair exposure problem: given limited intervention power of the platform, the goal is to enforce balance in the spread of content (e.g., news articles) among two groups of users through constraints similar to those imposed by the Fairness Doctrine in the United States in the past. Groups are characterized by different affiliations (e.g., political views) and have different preferences for content. We develop a stylized framework that models intra- and inter-group content propagation under homophily, and we formulate the platform's decision as an optimization problem that aims at maximizing user engagement, potentially under fairness constraints. Our main notion of fairness requires that each group see a mixture of their preferred and non-preferred content, encouraging information diversity. Promoting such information diversity is often viewed as desirable and a potential means for breaking out of harmful echo chambers. We study the solutions to both the fairness-agnostic and fairness-aware problems. We prove that a fairness-agnostic approach inevitably leads to group-homogeneous targeting  by the platform. This is only partially mitigated by imposing fairness constraints: we show that there exist optimal fairness-aware solutions which target one group with different types of content and the other group with only one type that is not necessarily the group's most preferred. Finally, using simulations with real-world data, we study the system dynamics and quantify the price of fairness.

Online Platforms and the Fair Exposure Problem Under Homophily

Visual question answering on document images that contain textual, visual, and layout information, called document VQA, has received much attention recently. Although many datasets have been proposed for developing document VQA systems, most of the existing datasets focus on understanding the content relationships within a single image and not across multiple images. In this study, we propose a new multi-image document VQA dataset, SlideVQA, containing 2.6k+ slide decks composed of 52k+ slide images and 14.5k questions about a slide deck. SlideVQA requires complex reasoning, including single-hop, multi-hop, and numerical reasoning, and also provides annotated arithmetic expressions of numerical answers for enhancing the ability of numerical reasoning. Moreover, we developed a new end-to-end document VQA model that treats evidence selection and question answering as a unified sequence-to-sequence format. Experiments on SlideVQA show that our model outperformed existing state-of-the-art QA models, but that it still has a large gap behind human performance. We believe that our dataset will facilitate research on document VQA.

SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images

In NLP annotation, it is common to have multiple annotators label the text and then obtain the ground truth labels based on major annotators’ agreement. However, annotators are individuals with different backgrounds and various voices. When annotation tasks become subjective, such as detecting politeness, offense, and social norms, annotators’ voices differ and vary. Their diverse voices may represent the true distribution of people’s opinions on subjective matters. Therefore, it is crucial to study the disagreement from annotation to understand which content is controversial from the annotators. In our research, we extract disagreement labels from five subjective datasets, then fine-tune language models to predict annotators’ disagreement. Our results show that knowing annotators’ demographic information (e.g., gender, ethnicity, education level), in addition to the task text, helps predict the disagreement. To investigate the effect of annotators’ demographics on their disagreement level, we simulate different combinations of their artificial demographics and explore the variance of the prediction to distinguish the disagreement from the inherent controversy from text content and the disagreement in the annotators’ perspective. Overall, we propose an innovative disagreement prediction mechanism for better design of the annotation process that will achieve more accurate and inclusive results for NLP systems. Our code and dataset are publicly available.

Attention-Conditioned Augmentations for Self-Supervised Anomaly Detection and Localization

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