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We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement distresses that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classification approaches in automatic pavement distress detection. The source codes of IOPLIN are released on https://github.com/DearCaat/ioplin , and the CQU-BPDD dataset is able to be accessed on https://dearcaat.github.io/CQU-BPDD/. [ABSTRACT FROM AUTHOR]

Although Out-Of-Distribution (OOD) detection has been extensively studied, the OOD detection under multi-label settings, which is closer to the real world, is still in its infancy. The pioneer work ignores some unique properties of multi-label images, such as the sparsity and co-occurrence of labels. Here, we empirically observe that these properties readily distinguish OOD and in-distribution data. Motivated by this observation, we propose a novel multi-label OOD detection approach named Sparse Label Co-occurrence Scoring (SLCS) to exploit the sparsity and co-occurrence information of labels. SLCS follows conventions and deems the logits outputted by the penultimate layer of the trained multi-label image classification model as the prediction confidences of a sample to categories in the training label set. A logit sparse filtering process is employed to filter out the low-confidence logits for avoiding the interference of low-confidence predictions while preserving the high-confidence logits to obtain the label sparsity. Then, the label co-occurrence pairs are counted for each sample based on its predicted categories and the label co-occurrence matrix constructed on the training set. Finally, the preserved logits are weighted by the label co-occurrence information and accumulated to produce the OOD detection score for each sample. Extensive experimental results on three well-known multi-label image datasets demonstrate the discriminating power of SLCS, which achieves greatly improved performances compared with the only multi-label OOD detection approach — JointEnergry and the state-of-the-art single-label OOD detection approaches. The performance improvements of SLCS over JointEnergy in FPR95 are 12.85%, 12.41%, and 9.50% on MS-COCO, VOC 2012, and NUS-WIDE datasets respectively. [ABSTRACT FROM AUTHOR]

Motivated by the fact that the signals tend to have a representation biased towards their own classes, we propose a novel Sparse Representation-based Classifier (SRC) named Class Specific Sparse Representation-based Classifier (CSSRC), which incorporates the class information in the representation learning. Unlike the conventional SRC algorithms, CSSRC defines each class as a group and then impels these groups to compete for representing the test sample. To achieve such property, CSSRC imposes a L 1 -norm constraint to the classes for compulsively selecting the most relevant classes and introduces a L 2 -norm constraint to the samples belonging to the same class for making sure that all homogeneous samples can be sufficiently exploited for representation. Since CSSRC is a typical structure sparse representation issue, it can be efficiently solved by the convex optimization. Seven popular visual and audio signal databases are employed for evaluation. The results demonstrate its effectiveness in comparison with the state-of-the-art classifiers. [ABSTRACT FROM AUTHOR]