Detecting semantic anomalies

We critically appraise the recent interest in out-of-distribution (OOD) detection and question the practical relevance of existing benchmarks. While the currently prevalent trend is to consider different datasets as OOD, we argue that out-distributions of practical interest are ones where the distinction is semantic in nature for a specified context, and that evaluative tasks should reflect this more closely. Assuming a context of object recognition, we recommend a set of benchmarks, motivated by practical applications. We make progress on these benchmarks by exploring a multi-task learning based approach, showing that auxiliary objectives for improved semantic awareness result in improved semantic anomaly detection, with accompanying generalization benefits.Comment: Preprint for AAAI '20 publicatio

Comparing autoencoder-based approaches for anomaly detection in highway driving scenario images

Autoencoder-based anomaly detection approaches can be used for precluding scope compliance failures of the automotive perception. However, the applicability of these approaches for the automotive domain should be thoroughly investigated. We study the capability of two autoencoder-based approaches using reconstruction errors and bottleneck-values for detecting semantic anomalies in automotive images. As a use-case, we consider a specific highway driving scenario identifying if there are any vehicles in the field of view of a front-looking camera. We conduct a series of experiments with two simulated driving scenario datasets and measure anomaly detection performance for different cases. We systematically test different autoencoders and training parameters, as well as the influence of image colors. We show that the autoencoder-based approaches demonstrate promising results for detecting semantic anomalies in highway driving scenario images in some cases. However, we also observe the variability of anomaly detection performance between different experiments. The autoencoder-based approaches are capable of detecting semantic anomalies in highway driving scenario images to some extent. However, further research with other use-cases and real datasets is needed before they can be safely applied in the automotive domain

Detecting the Unexpected via Image Resynthesis

Classical semantic segmentation methods, including the recent deep learning ones, assume that all classes observed at test time have been seen during training. In this paper, we tackle the more realistic scenario where unexpected objects of unknown classes can appear at test time. The main trends in this area either leverage the notion of prediction uncertainty to flag the regions with low confidence as unknown, or rely on autoencoders and highlight poorly-decoded regions. Having observed that, in both cases, the detected regions typically do not correspond to unexpected objects, in this paper, we introduce a drastically different strategy: It relies on the intuition that the network will produce spurious labels in regions depicting unexpected objects. Therefore, resynthesizing the image from the resulting semantic map will yield significant appearance differences with respect to the input image. In other words, we translate the problem of detecting unknown classes to one of identifying poorly-resynthesized image regions. We show that this outperforms both uncertainty- and autoencoder-based methods

Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection

This paper presents a novel framework, named Global-Local Correspondence Framework (GLCF), for visual anomaly detection with logical constraints. Visual anomaly detection has become an active research area in various real-world applications, such as industrial anomaly detection and medical disease diagnosis. However, most existing methods focus on identifying local structural degeneration anomalies and often fail to detect high-level functional anomalies that involve logical constraints. To address this issue, we propose a two-branch approach that consists of a local branch for detecting structural anomalies and a global branch for detecting logical anomalies. To facilitate local-global feature correspondence, we introduce a novel semantic bottleneck enabled by the visual Transformer. Moreover, we develop feature estimation networks for each branch separately to detect anomalies. Our proposed framework is validated using various benchmarks, including industrial datasets, Mvtec AD, Mvtec Loco AD, and the Retinal-OCT medical dataset. Experimental results show that our method outperforms existing methods, particularly in detecting logical anomalies.Comment: Submission to IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOG

Using Semantic Ambiguity Instruction to Improve Third Graders\u27 Metalinguistic Awareness and Reading Comprehension: An Experimental Study

An experiment examined whether metalinguistic awareness involving the detection of semantic ambiguity can be taught and whether this instruction improves students\u27 reading comprehension. Lower socioeconomic status third graders (M age = 8 years, 7 months) from a variety of cultural backgrounds (N = 46) were randomly assigned to treatment and control groups. Those receiving metalinguistic ambiguity instruction learned to analyze multiple meanings of words and sentences in isolation, in riddles, and in text taken from the Amelia Bedelia series (Parish, 1979, 988). The control group received a book-reading and discussion treatment to provide special attention and to rule out Hawthorne effects. Results showed that metalinguistic ambiguity instruction was effective in teaching students to identify multiple meanings of homonyms and ambiguous sentences and to detect inconsistencies in text. Moreover, this training enhanced students\u27 reading com prehension on a paragraph-completion task but not on a multiple-choice passage-recall task, possibly because the two tests differ in the array of linguistic or cognitive correlates influencing performance. Comprehension monitoring was not found to mediate the relationship between ambiguity instruction and reading comprehension. Results carry implications for the use of language-based methods to improve reading comprehension in the classroom

Map++: A Crowd-sensing System for Automatic Map Semantics Identification

Digital maps have become a part of our daily life with a number of commercial and free map services. These services have still a huge potential for enhancement with rich semantic information to support a large class of mapping applications. In this paper, we present Map++, a system that leverages standard cell-phone sensors in a crowdsensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others. Our analysis shows that cell-phones sensors with humans in vehicles or walking get affected by the different road features, which can be mined to extend the features of both free and commercial mapping services. We present the design and implementation of Map++ and evaluate it in a large city. Our evaluation shows that we can detect the different semantics accurately with at most 3% false positive rate and 6% false negative rate for both vehicle and pedestrian-based features. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.Comment: Published in the Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (IEEE SECON 2014

Towards an ontology for process monitoring and mining

Business Process Analysis (BPA) aims at monitoring, diagnosing, simulating and mining enacted processes in order to support the analysis and enhancement of process models. An effective BPA solution must provide the means for analysing existing e-businesses at three levels of abstraction: the Business Level, the Process Level and the IT Level. BPA requires semantic information that spans these layers of abstraction and which should be easily retrieved from audit trails. To cater for this, we describe the Process Mining Ontology and the Events Ontology which aim to support the analysis of enacted processes at different levels of abstraction spanning from fine grain technical details to coarse grain aspects at the Business Level