Self-supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes

Robust autonomous driving requires agents to accurately identify unexpected areas in urban scenes. To this end, some critical issues remain open: how to design advisable metric to measure anomalies, and how to properly generate training samples of anomaly data? Previous effort usually resorts to uncertainty estimation and sample synthesis from classification tasks, which ignore the context information and sometimes requires auxiliary datasets with fine-grained annotations. On the contrary, in this paper, we exploit the strong context-dependent nature of segmentation task and design an energy-guided self-supervised frameworks for anomaly segmentation, which optimizes an anomaly head by maximizing the likelihood of self-generated anomaly pixels. To this end, we design two estimators for anomaly likelihood estimation, one is a simple task-agnostic binary estimator and the other depicts anomaly likelihood as residual of task-oriented energy model. Based on proposed estimators, we further incorporate our framework with likelihood-guided mask refinement process to extract informative anomaly pixels for model training. We conduct extensive experiments on challenging Fishyscapes and Road Anomaly benchmarks, demonstrating that without any auxiliary data or synthetic models, our method can still achieves competitive performance to other SOTA schemes

Learning with Noisy labels via Self-supervised Adversarial Noisy Masking

Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithms. Previous efforts tend to mitigate this problem via identifying and removing noisy samples or correcting their labels according to the statistical properties (e.g., loss values) among training samples. In this paper, we aim to tackle this problem from a new perspective, delving into the deep feature maps, we empirically find that models trained with clean and mislabeled samples manifest distinguishable activation feature distributions. From this observation, a novel robust training approach termed adversarial noisy masking is proposed. The idea is to regularize deep features with a label quality guided masking scheme, which adaptively modulates the input data and label simultaneously, preventing the model to overfit noisy samples. Further, an auxiliary task is designed to reconstruct input data, it naturally provides noise-free self-supervised signals to reinforce the generalization ability of deep models. The proposed method is simple and flexible, it is tested on both synthetic and real-world noisy datasets, where significant improvements are achieved over previous state-of-the-art methods

Rethinking Mobile Block for Efficient Attention-based Models

This paper focuses on developing modern, efficient, lightweight models for dense predictions while trading off parameters, FLOPs, and performance. Inverted Residual Block (IRB) serves as the infrastructure for lightweight CNNs, but no counterpart has been recognized by attention-based studies. This work rethinks lightweight infrastructure from efficient IRB and effective components of Transformer from a unified perspective, extending CNN-based IRB to attention-based models and abstracting a one-residual Meta Mobile Block (MMB) for lightweight model design. Following simple but effective design criterion, we deduce a modern Inverted Residual Mobile Block (iRMB) and build a ResNet-like Efficient MOdel (EMO) with only iRMB for down-stream tasks. Extensive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, e.g., EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass equal-order CNN-/Attention-based models, while trading-off the parameter, efficiency, and accuracy well: running 2.8-4.0x faster than EdgeNeXt on iPhone14

AR-Miner: Mining informative reviews for developers from mobile app marketplace

Ministry of Education, Singapore under its Academic Research Funding Tier

Deep learning with application to hashing

Deep Learning and Learning to Hash are two important research areas in machine learning, which have rapid improvements in recent years. What I mainly researched on is an inter-discipline field: deep learning for cross view hashing. Multiple layers of representation in deep learning has the property of abstracting representation from input data, while, in the cross view similarity search, the biggest difficulty is to represent items from one domain to another. Here, I want to take advantage of the latest deep learning technology to solve the cross view similarity search problem. Hashing is used to accelerate this process. This thesis mainly contains three parts. Chapter 2 is a literature survey. It contains a deep learning survey and a learning to hash survey. The deep learning survey briefly introduces fundamental technology of deep learning and its recent development including the latest technology. The Learning to Hash survey brief introduces some widely used learning to hash algorithms. Chapter 3 is an experiment about comparison of some state of the arts learning to hash algorithms. Chapter 4 is cross view hashing based on deep learning. I present a cross view feature hashing technique using deep learning and show some results. These three chapters are main chapters. Chapter 1 and Chapter 5 are introduction and conclusion.MASTER OF ENGINEERING (SCE

Health Condition Assessment of Marine Systems Based on an Improved Radar Chart

Since health assessment plays a significant role in marine systems (MSs), it has caught the attention of researchers. In this study, a powerful evaluation method called an improved radar chart was developed as a means of reliability estimation. General evaluation methods applied in the comprehensive evaluation of MS are slightly insufficient in terms of considering index coordination. However, the application of a radar chart can solve this problem. To improving the shortcomings of a traditional radar chart, the fuzzy centralization statistical theory and the entropy weight were combined in this study to obtain the comprehensive weight. The weight could be converted into an angle, and it could reflect the influence degree of the indexes on the evaluation objects. Additionally, an angle bisector was introduced as an index axis, and the eigenvector was extracted to get the unique evaluation result. The result showed that the proposed method could achieve the continuous online monitoring of the system state, and the reliable and accurate assessment results were able to provide a reference for condition-based maintenance and decision-making

PCViT: A Pre-Convolutional ViT Coal Gangue Identification Method

For the study of coal and gangue identification using near-infrared reflection spectroscopy, samples of anthracite coal and gangue with similar appearances were collected, and different dust concentrations (200 ug/m3, 500 ug/m3 and 800 ug/m3), detection distances (1.2 m, 1.5 m and 1.8 m) and mixing gangue rates (one-third coal, two-thirds coal, full coal) were collected in the laboratory by the reflection spectroscopy acquisition device and the gangue reflection spectral data. The spectral data were pre-processed using three methods, first-order differentiation, second-order differentiation and standard normal variable transformation, in order to enhance the absorption characteristics of the reflectance spectra and to eliminate the effects of changes in the experimental environment. The PCViT gangue identification model is established, and the disadvantages of the violent patch embedding of the ViT model are improved by using the stepwise convolution operation to extract features. Then, the interdependence of the features of the hyperspectral data is modeled by the self-attention module, and the learned features are optimized adaptively. The results of gangue recognition under nine working conditions show that the proposed recognition model can significantly improve the recognition accuracy, and this study can provide a reference value for gangue recognition using the near-infrared reflection spectra of gangue

Single-Nucleotide Polymorphisms in XPO5 are Associated with Noise-Induced Hearing Loss in a Chinese Population

Objectives.The purpose of this study was to investigate the correlation between single-nucleotide polymorphism (SNP) in 3′UTR of XPO5 gene and the occurrence of noise-induced hearing loss (NIHL), and to further explore the regulatory mechanism of miRNAs in NIHL on XPO5 gene. Methods.We conducted a case-control study involving 1040 cases and 1060 controls. The effects of SNPs on XPO5 expression were studied by genotyping, real-time polymerase chain reaction (qPCR), cell transfection, and the dual-luciferase reporter assay. Results.We genotyped four SNPs (rs2257082, rs11077, rs7755135, and rs1106841) in the XPO5 gene. The rs2257082 AG/GG carriers have special connection to an increased risk of noise-induced hearing loss compared to the AA carriers. The rs11077TG/GG carriers had a significantly increased association with NIHL susceptibility than the TT carriers. There was a higher risk of NIHL in the XPO5 gene rs7755135 CC carriers than in the TT carriers. No statistically significant correlation was obtained with respect to SNPrs1106841. Functional experiments showed that the rs11077 change might inhibit the interaction between miRNAs (miRNA-4763-5p, miRNA-5002-3p, and miRNA-617) and XPO5, with rs11077G allele resulting in overexpression of XPO5. Conclusion. The genetic polymorphism, rs11077, within XPO5 is associated with the risk of noise-induced hearing loss in a Chinese population