Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection

This paper presents a novel method that leverages a visual-language model, CLIP, as a data source for zero-shot anomaly detection. Tremendous efforts have been put towards developing anomaly detectors due to their potential industrial applications. Considering the difficulty in acquiring various anomalous samples for training, most existing methods train models with only normal samples and measure discrepancies from the distribution of normal samples during inference, which requires training a model for each object category. The problem of this inefficient training requirement has been tackled by designing a CLIP-based anomaly detector that applies prompt-guided classification to each part of an image in a sliding window manner. However, the method still suffers from the labor of careful prompt ensembling with known object categories. To overcome the issues above, we propose leveraging CLIP as a data source for training. Our method generates text embeddings with the text encoder in CLIP with typical prompts that include words of normal and anomaly. In addition to these words, we insert several randomly generated words into prompts, which enables the encoder to generate a diverse set of normal and anomalous samples. Using the generated embeddings as training data, a feed-forward neural network learns to extract features of normal and anomaly from CLIP's embeddings, and as a result, a category-agnostic anomaly detector can be obtained without any training images. Experimental results demonstrate that our method achieves state-of-the-art performance without laborious prompt ensembling in zero-shot setups.Comment: Accepted to BMVC202

Segmentation-Based Bounding Box Generation for Omnidirectional Pedestrian Detection

We propose a segmentation-based bounding box generation method for omnidirectional pedestrian detection that enables detectors to tightly fit bounding boxes to pedestrians without omnidirectional images for training. Due to the wide angle of view, omnidirectional cameras are more cost-effective than standard cameras and hence suitable for large-scale monitoring. The problem of using omnidirectional cameras for pedestrian detection is that the performance of standard pedestrian detectors is likely to be substantially degraded because pedestrians' appearance in omnidirectional images may be rotated to any angle. Existing methods mitigate this issue by transforming images during inference. However, the transformation substantially degrades the detection accuracy and speed. A recently proposed method obviates the transformation by training detectors with omnidirectional images, which instead incurs huge annotation costs. To obviate both the transformation and annotation works, we leverage an existing large-scale object detection dataset. We train a detector with rotated images and tightly fitted bounding box annotations generated from the segmentation annotations in the dataset, resulting in detecting pedestrians in omnidirectional images with tightly fitted bounding boxes. We also develop pseudo-fisheye distortion augmentation, which further enhances the performance. Extensive analysis shows that our detector successfully fits bounding boxes to pedestrians and demonstrates substantial performance improvement.Comment: Pre-print submitted to Journal of Multimedia Tools and Application

Development of a Typing Skill Learning Environment with Diagnosis and Advice on Fingering Errors

AbstractExisting application software for touch typing training cannot diagnose fingering errors. Given this fact, we developed a skill learning environment for touch typing training that can diagnose fingering errors by recognizing fingers with color markers using image recognition technique. This study developed two systems: a learning support environment for an experimental group and a learning environment for a control group. We evaluated the effect of the learning environment that can diagnose fingering errors for the experimental group, by comparison with the other learning environment for the control group

The PCR-Based Diagnosis of Central Nervous System Tuberculosis: Up to Date

Central nervous system (CNS) tuberculosis, particularly tuberculous meningitis (TBM), is the severest form of Mycobacterium tuberculosis (M.Tb) infection, causing death or severe neurological defects in more than half of those affected, in spite of recent advancements in available anti-tuberculosis treatment. The definitive diagnosis of CNS tuberculosis depends upon the detection of M.Tb bacilli in the cerebrospinal fluid (CSF). At present, the diagnosis of CNS tuberculosis remains a complex issue because the most widely used conventional “gold standard” based on bacteriological detection methods, such as direct smear and culture identification, cannot rapidly detect M.Tb in CSF specimens with sufficient sensitivity in the acute phase of TBM. Recently, instead of the conventional “gold standard”, the various molecular-based methods including nucleic acid amplification (NAA) assay technique, particularly polymerase chain reaction (PCR) assay, has emerged as a promising new method for the diagnosis of CNS tuberculosis because of its rapidity, sensitivity and specificity. In addition, the innovation of nested PCR assay technique is worthy of note given its contribution to improve the diagnosis of CNS tuberculosis. In this review, an overview of recent progress of the NAA methods, mainly highlighting the PCR assay technique, was presented

Hunting Group Clues with Transformers for Social Group Activity Recognition

This paper presents a novel framework for social group activity recognition. As an expanded task of group activity recognition, social group activity recognition requires recognizing multiple sub-group activities and identifying group members. Most existing methods tackle both tasks by refining region features and then summarizing them into activity features. Such heuristic feature design renders the effectiveness of features susceptible to incomplete person localization and disregards the importance of scene contexts. Furthermore, region features are sub-optimal to identify group members because the features may be dominated by those of people in the regions and have different semantics. To overcome these drawbacks, we propose to leverage attention modules in transformers to generate effective social group features. Our method is designed in such a way that the attention modules identify and then aggregate features relevant to social group activities, generating an effective feature for each social group. Group member information is embedded into the features and thus accessed by feed-forward networks. The outputs of feed-forward networks represent groups so concisely that group members can be identified with simple Hungarian matching between groups and individuals. Experimental results show that our method outperforms state-of-the-art methods on the Volleyball and Collective Activity datasets.Comment: Accepted to ECCV202

Radiotherapy for cancer using X-ray fluorescence emitted from iodine

Radiation treatment is popular and the apparatus is already available in many hospitals. Conventional radiation treatment by itself is not sufficient to achieve complete cure. Therefore, radiosensitizers have been developed to enhance the therapeutic effects of the treatment. The concept of radiosensitization with high-Z-elements was first considered many decades ago. However, radiosensitizers are not commonly used in the clinical setting. Here, we propose a radiotherapy method that utilizes fluorescent X-ray emissions from iodine. This approach should achieve a greater therapeutic effect than that of conventional radiotherapy treatments. In our radiotherapy, iomeprol was used as the iodine-donor. The X-ray apparatus with copper and aluminum filters could be used for the X-ray irradiation, the apparatus is not needed for exclusive use. The X-ray apparatus is only required to prepare the copper and aluminum filters. As proof-of-concept, we show that tumor growth was attenuated using this treatment with iomeprol

An Improved DDS Tool with Versatile Cell-targeting Ability

Background/Aim: The aim of this study was to develop an improved drug delivery system (DDS) tool with enhanced versatility in the cell-targeting step using as Z-domain, a modified IgG binding domain of protein A from Staphylococcus aureus, as an IgG adapter domain. Materials and Methods: The chimera protein expression system composed of the Z-domain and chimeric cholesterol-dependent cytolysin mutant named His-Z-CDC(ss)IS was constructed in Escherichia coli. His-Z-CDC(ss)IS was purified by Ni-affinity chromatography, and its abilities for controlled pore formation, membrane binding, IgG binding, and target cell-specific delivery of liposomes carrying medicine were investigated. Results and Discussion: His-Z-CDC(ss)IS purified by Ni-affinity chromatography indicated pore-forming activity only under disulfide bond reducing conditions. His-Z-CDC(ss)IS also demonstrated an ability to bind both IgG and cholesterol-embedded liposomes via its Z-domain and domain 4, respectively. Furthermore, anticarcinoembryonic antigen (CEA) IgG-bound His-Z-CDC(ss)IS indicated effective delivery of liposomes carrying drugs to CEA-expressing cells. Conclusion: His-Z-CDC(ss)IS was revealed to be an improved DDS tool with enhanced versatility in cell targeting