Physical Adversarial Attack Meets Computer Vision: A Decade Survey.

Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision, their vulnerability to adversarial attacks remains a critical concern. Extensive research has demonstrated that incorporating sophisticated perturbations into input images can lead to a catastrophic degradation in DNNs' performance. This perplexing phenomenon not only exists in the digital space but also in the physical world.

Win-Win by Competition: Auxiliary-Free Cloth-Changing Person Re-Identification.

Recent person Re-IDentification (ReID) systems have been challenged by changes in personnel clothing, leading to the study of Cloth-Changing person ReID (CC-ReID). Commonly used techniques involve incorporating auxiliary information (e.g.

Towards Robust Person Re-Identification by Defending Against Universal Attackers.

Recent studies show that deep person re-identification (re-ID) models are vulnerable to adversarial examples, so it is critical to improving the robustness of re-ID models against attacks. To achieve this goal, we explore the strengths and weaknesses of existing re-ID models, i.e.

Unsupervised Foggy Scene Understanding via Self Spatial-Temporal Label Diffusion.

Understanding foggy image sequence in driving scene is critical for autonomous driving, but it remains a challenging task due to the difficulty in collecting and annotating real-world images of adverse weather. Recently, self-training strategy has been considered as a powerful solution for unsupervised domain adaptation, which iteratively adapts the model from the source domain to the target domain by generating target pseudo labels and re-training the model. However, the selection of confident pseudo labels inevitably suffers from the conflict between sparsity and accuracy, both of which will lead to suboptimal models.

Weakly-Supervised Learning With Complementary Heatmap for Retinal Disease Detection.

There are many types of retinal disease, and accurately detecting these diseases is crucial for proper diagnosis. Convolutional neural networks (CNNs) typically perform well on detection tasks, and the attention module of CNNs can generate heatmaps as visual explanations of the model. However, the generated heatmap can only detect the most discriminative part, which is problematic because many object regions may exist in the region beside the heatmap in an area known as a complementary heatmap.

Classification of large-scale image database of various skin diseases using deep learning.

Purpose: The purpose of this study was to develop a deep learning-based computer-aided diagnosis system for skin disease classification using photographic images of patients. The targets are 59 skin diseases, including localized and diffuse diseases captured by photographic cameras, resulting in highly diverse images in terms of the appearance of the diseases or photographic conditions.

Methods: ResNet-18 is used as a baseline model for classification and is reinforced by metric learning to boost generalization in classification by avoiding the overfitting of the training data and increasing the reliability of CADx for dermatologists.

MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction.

Background: Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's disease (AD).

How to Extract More Information With Less Burden: Fundus Image Classification and Retinal Disease Localization With Ophthalmologist Intervention.

Image classification using convolutional neural networks (CNNs) outperforms other state-of-the-art methods. Moreover, attention can be visualized as a heatmap to improve the explainability of results of a CNN. We designed a framework that can generate heatmaps reflecting lesion regions precisely.

Signal-to-Noise Ratio of Brillouin Grating Measurement with Micrometer-Resolution Optical Low Coherence Reflectometry.

Signal-dependent speckle-like noise was the dominant noise in a Brillouin grating measurement with micrometer-resolution optical low coherence reflectometry (OLCR). The noise was produced by the interaction of a Stokes signal with beat noise caused by a leaked pump light via square-law detection. The resultant signal-to-noise ratio (SNR) was calculated and found to be proportional to the square root of the dynamic range (DR) defined by the ratio of the Stokes signal magnitude to the variance of the beat noise.

Learning Sparse and Identity-Preserved Hidden Attributes for Person Re-Identification.

Person re-identification (Re-ID) aims at matching person images captured in non-overlapping camera views. To represent person appearance, low-level visual features are sensitive to environmental changes, while high-level semantic attributes, such as "short-hair" or "long-hair", are relatively stable. Hence, researches have started to design semantic attributes to reduce the visual ambiguity.

Evaluation of deep convolutional neural networks for glaucoma detection.

Purpose: To investigate the performance of deep convolutional neural networks (DCNNs) for glaucoma discrimination using color fundus images STUDY DESIGN: A retrospective study PATIENTS AND METHODS: To investigate the discriminative ability of 3 DCNNs, we used a total of 3312 images consisting of 369 images from glaucoma-confirmed eyes, 256 images from glaucoma-suspected eyes diagnosed by a glaucoma expert, and 2687 images judged to be nonglaucomatous eyes by a glaucoma expert. We also investigated the effects of image size on the discriminative ability and heatmap analysis to determine which parts of the image contribute to the discrimination. Additionally, we used 465 poor-quality images to investigate the effect of poor image quality on the discriminative ability.

Measurement of a true Brillouin grating distribution generated at mated fiber connectors with optical low coherence reflectometry coupled with dispersive Fourier spectroscopy.

We previously reported a reflectogram from mated fiber connectors that was measured at a spatial resolution of 100 μm with Brillouin-gating-based optical low coherence reflectometry, and that agreed with a theoretical curve calculated by assuming that there was a step-like Brillouin grating distribution [Electron. Lett.53, 423 (2017)].

Beat noise reduction utilizing the transient acoustic-wave response of an optical fiber in Brillouin grating-based optical low coherence reflectometry.

We periodically generated a transient Brillouin grating by using continuous-wave pump light and 50 MHz pulse-wave pump light with optical low coherence reflectometry (OLCR). We extracted the Stokes light generated by the decaying part of the grating with an optical switch, and this enabled us to block the pulse-wave pump light from entering the balanced mixer, resulting in a reduction in the noise caused by the beat between the local oscillator light and the pump light. For the first time, to the best of our knowledge, we succeeded in detecting a Stokes light with an OLCR, despite the fact that the states of polarization of the probe and pump light waves were parallel, and this encouraged us to construct a polarization-independent OLCR for diagnosing optical modules and three-dimensional objects.

Person Reidentification via Discrepancy Matrix and Matrix Metric.

Person reidentification (re-id), as an important task in video surveillance and forensics applications, has been widely studied. Previous research efforts toward solving the person re-id problem have primarily focused on constructing robust vector description by exploiting appearance's characteristic, or learning discriminative distance metric by labeled vectors. Based on the cognition and identification process of human, we propose a new pattern, which transforms the feature description from characteristic vector to discrepancy matrix.

Instance Search Retrospective with Focus on TRECVID.

This paper presents an overview of the Video Instance Search benchmark which was run over a period of 6 years (2010-2015) as part of the TREC Video Retrieval (TRECVID) workshop series. The main contributions of the paper include i) an examination of the evolving design of the evaluation framework and its components (system tasks, data, measures); ii) an analysis of the influence of topic characteristics (such as rigid/non rigid, planar/non-planar, stationary/mobile on performance; iii) a high-level overview of results and best-performing approaches. The Instance Search (INS) benchmark worked with a variety of large collections of data including Sound & Vision, Flickr, BBC (British Broadcasting Corporation) Rushes for the first 3 pilot years and with the small world of the BBC Eastenders series for the last 3 years.

Web Image Search Re-Ranking With Click-Based Similarity and Typicality.

In image search re-ranking, besides the well-known semantic gap, intent gap, which is the gap between the representation of users' query/demand and the real intent of the users, is becoming a major problem restricting the development of image retrieval. To reduce human effects, in this paper, we use image click-through data, which can be viewed as the implicit feedback from users, to help overcome the intention gap, and further improve the image search performance. Generally, the hypothesis-visually similar images should be close in a ranking list-and the strategy-images with higher relevance should be ranked higher than others-are widely accepted.

When face-tracking meets social networks: a story of politics in news videos.

In the age of data processing, news videos are rich mines of information. After all, the news are essentially created to convey information to the public. But can we go beyond what is directly presented to us and see a wider picture? Many works already focus on what we can discover and understand from the analysis of years of news broadcasting.

Pleiotropic effects of the rho-kinase inhibitor fasudil after subarachnoid hemorrhage: a review of preclinical and clinical studies.

There is growing evidence that Rho-kinase contributes to cardiovascular disease, which has made Rho-kinase a target for the treatment of human diseases. To date, the only Rho-kinase inhibitor employed clinically in humans is fasudil, which has been used for the prevention of cerebral vasospasm and subsequent ischemic injury after surgery for subarachnoid hemorrhage (SAH). A number of pathological processes, in particular hemodynamic dysfunctions and inflammatory reactions, are thought to be related in the pathogenesis of delayed cerebral vasospasm and subsequent ischemic injury after SAH.

Evidence of a direct cellular protective effect of Rho-kinase inhibitors on endothelin-induced cardiac myocyte hypertrophy.

Using a cellular approach, the present study examined whether fasudil and active metabolite hydroxyfasudil, Rho-kinase inhibitors, exert a direct protective effect on endothelin-induced cardiac myocyte hypertrophy in vitro. Treatment with endothelin (10nM) caused significant hypertrophy of cultured neonatal rat cardiomyocytes by a 21.2% increase in cell surface area.

Antivasospastic Effects of Hydroxyfasudil, a Rho-Kinase Inhibitor, After Subarachnoid Hemorrhage.

We investigated the anti-vasospastic potential of fasudil's active metabolite, hydroxyfasudil, a Rho-kinase inhibitor, after subarachnoid hemorrhage (SAH) and also its effect on hemorheological abnormalities following cerebral ischemia. Chronic cerebral vasospasm was produced using a two-hemorrhage canine model. On day 7, angiographic vasospasm was observed in all animals, and intravenous administration of hydroxyfasudil (3 mg·kg·30 min) significantly reversed the vasospasm (predose diameter of the basilar artery, 57.

Antivasospastic effects of hydroxyfasudil, a Rho-kinase inhibitor, after subarachnoid hemorrhage.

We investigated the anti-vasospastic potential of fasudil's active metabolite, hydroxyfasudil, a Rho-kinase inhibitor, after subarachnoid hemorrhage (SAH) and also its effect on hemorheological abnormalities following cerebral ischemia. Chronic cerebral vasospasm was produced using a two-hemorrhage canine model. On day 7, angiographic vasospasm was observed in all animals, and intravenous administration of hydroxyfasudil (3 mg·kg(-1)·30 min(-1)) significantly reversed the vasospasm (predose diameter of the basilar artery, 57.

Identification of MIG12 as a mediator for stimulation of lipogenesis by LXR activation.

Liver X receptor (LXR)α and LXRβ belong to the nuclear receptor superfamily and play central roles in the transcriptional control of lipid metabolism. We describe a novel LXR target, midline-1-interacting G12-like protein (MIG12), which has been recently identified as an acetyl-coenzyme A carboxylase-binding protein. The binding causes the induction of de novo fatty acid (FA) synthesis through the activation of acetyl-coenzyme A carboxylase (a rate-limiting enzyme for de novo FA synthesis).

Fasudil protects cultured N1E-115 cells against lysophosphatidic acid-induced neurite retraction through inhibition of Rho-kinase.

The aim of this study was to investigate the possible effects of the Rho-kinase inhibitor, fasudil, on the lysophosphatidic acid (LPA)-induced neurite retraction in N1E-115 cells. In cultured N1E-115 cells, LPA produced a marked increase in the population of rounded cells. Fasudil or hydroxyfasudil, an active metabolite of fasudil, blocked cell rounding in a concentration-dependent manner at levels between 1 and 10 μM, with IC₅₀ values of 1.

Amelioration of endothelial damage/dysfunction is a possible mechanism for the neuroprotective effects of Rho-kinase inhibitors against ischemic brain damage.

We investigated the neuroprotective effects of fasudil's active metabolite, hydroxyfasudil, a Rho-kinase inhibitor, in a rat stroke model in which endothelial damage and subsequent thrombotic occlusion were selectively induced in perforating arteries. By examining the effects on the endothelial damage/dysfunction, we thought to explore the mechanism of Rho-kinase inhibitors. Hydroxyfasudil (10mg/kg, i.

Correspondence between neurological deficit, cerebral infarct size, and Rho-kinase activity in a rat cerebral thrombosis model.

Whether Rho-kinase activity is really associated with the pathogenesis of cerebral infarction remains unclear. To consider this question, we investigated correspondences between severity of neurological deficit, infarct size, amount of various marker proteins, and Rho-kinase activity in a rat cerebral infarction model. Sodium laurate was injected into the left internal carotid artery, inducing cerebral infarction in the ipsilateral hemisphere in rats.