Anomaly detection plays an important role in manufacturing quality control/assurance. Among approaches adopting computer vision techniques, reconstruction-based methods learn a content-aware mapping function that transfers abnormal regions to normal regions in an unsupervised manner. Such methods usually have difficulty in improving both the reconstruction quality and capacity for abnormal discovery. We observe that high-level semantic contextual features demonstrate a strong ability for abnormal discovery, while variational features help to preserve fine image details. Inspired by the observation, we propose a new abnormal detection model by utilizing features for different purposes depending on their frequency characteristics. The 2D-discrete wavelet transform (DWT) is introduced to obtain the low-frequency and high-frequency components of features and further used to generate the two essential features following different routing paths in our encoder process. To further improve the capacity for abnormal discovery, we propose a novel feature augmentation module that is informed by a customized self-attention mechanism. Extensive experiments are conducted on two popular datasets: MVTec AD and BTAD. The experimental results illustrate that the proposed method outperforms other state-of-the-art approaches in terms of the image-level AUROC score. In particular, our method achieves 100% of the image-level AUROC score on 8 out of 15 classes on the MVTec dataset.

Due to recent advances in learning-based semantic segmentation, road scene parsing can usually achieve satisfactory results under normal illumination conditions. However, training a robust model for parsing nighttime road scenes is still very challenging, especially when semantic labels of training samples are absent. In this paper, we propose a convolutional neural network (CNN)-based method for parsing nighttime road scenes in an unsupervised manner. The proposed system includes an appearance transferring module and a segmentation module, which are coupled together and learned in an end-to-end fashion. The appearance transferring module aims to transfer unlabeled images acquired during both daytime and nighttime into a shared latent feature space that encodes the image content of both scenes at the semantic level. Then, the segmentation module is used to map the feature to its corresponding semantic labels. To better evaluate the proposed model, we also construct a new semantic segmentation dataset including 1,566 nighttime images. The extensive experimental results on the proposed benchmark illustrate that the proposed model achieves significant improvement compared with the baselines as well as a recently released system.

We design a pedestrian proposal and refining system tailored for fast pedestrian detection. The pedestrian proposal is based on pixel differential feature (PDF), which is a light weighted feature with a high recall rate. For the pedestrian refining, we propose an aggregated region feature (ARF) to distill the co-existing dominant pixel differential patterns in a local region to reject hard false positives. Albeit discriminative, ARF largely relies on the size of anchored regions and the scale of the PDF, which hinders its performance in real-world applications. Although multi-scale PDF with spatial pyramid somewhat alleviates this problem, it is computationally expensive and thus infeasible in practice. To address this issue, we further propose a directional radius pooling method to extract discriminative information in each orientation of PDF while reducing the feature dimensionality with a more compact size. The pedestrian proposal and refining framework is built on the shared pixel differential feature map which is very computationally efficient. More specifically, a set of pedestrian proposals generated from the single-scale PDF are first obtained in images. Second, multi-scale ARF in spatial pyramid is used to fuse information from different scales and spatial resolutions for anchor regions. Third, the directional radius pooling method is proposed to extract dominant information of each orientation in the anchor regions. The pedestrian proposal and refining are finally integrated for accurate pedestrian detection. The extensive experimental evaluations on five public benchmarks show that our method achieves state-of-the-art results while running at 18 fps for $480\times640$ images.

In this paper we propose a new salient object detection method via structured label prediction. By learning appearance features in rectangular regions, our structural region representation encodes the local saliency distribution with a matrix of binary labels. We show that the linear combination of structured labels can well model the saliency distribution in local regions. Representing region saliency with structured labels has two advantages: 1) it connects the label assignment of all enclosed pixels, which produces a smooth saliency prediction; and 2) regularshaped nature of structured labels enables well definition of traditional cues such as regional properties and center surround contrast, and these cues help to build meaningful and informative saliency measures. To measure the consistency between a structured label and the corresponding saliency distribution, we further propose an adaptive label ranking algorithm using proposals that are generated by a CNN model. Finally, we introduce a K-NN enhanced graph representation for saliency propagation, which is more favorable for our task than the widely-used adjacent-graph-based ones. Experimental results demonstrate the effectiveness of our proposed method on six popular benchmarks compared with state-of-the-art approaches.

Despite the previous efforts of object proposals, the detection rates of the existing approaches are still not satisfactory enough. To address this, we propose Adobe Boxes to efficiently locate the potential objects with fewer proposals, in terms of searching the object adobes that are the salient object parts easy to be perceived. Because of the visual difference between the object and its surroundings, an object adobe obtained from the local region has a high probability to be a part of an object, which is capable of depicting the locative information of the proto-object. Our approach comprises of three main procedures. First, the coarse object proposals are acquired by employing randomly sampled windows. Then, based on local-contrast analysis, the object adobes are identified within the enlarged bounding boxes that correspond to the coarse proposals. The final object proposals are obtained by converging the bounding boxes to tightly surround the object adobes. Meanwhile, our object adobes can also refine the detection rate of most state-of-the-art methods as a refinement approach. The extensive experiments on four challenging datasets (PASCAL VOC2007, VOC2010, VOC2012, and ILSVRC2014) demonstrate that the detection rate of our approach generally outperforms the state-of-the-art methods, especially with relatively small number of proposals. The average time consumed on one image is about 48 ms, which nearly meets the real-time requirement.

This paper presents a new method for salient object detection based on a sophisticated appearance comparison of multisize superpixels. Those superpixels are modeled by multivariate normal distributions in CIE-Lab color space, which are estimated from the pixels they comprise. This fitting facilitates an efficient application of the Wasserstein distance on the Euclidean norm ( [Formula: see text]) to measure perceptual similarity between elements. Saliency is computed in two ways. On the one hand, we compute global saliency by probabilistically grouping visually similar superpixels into clusters and rate their compactness. On the other hand, we use the same distance measure to determine local center-surround contrasts between superpixels. Then, an innovative locally constrained random walk technique that considers local similarity between elements balances the saliency ratings inside probable objects and background. The results of our experiments show the robustness and efficiency of our approach against 11 recently published state-of-the-art saliency detection methods on five widely used benchmark data sets.

We present a new segment-based method for saliency detection based on multi-size superpixels that combines local and global saliency cues. We extract superpixels at several scales and represent each superpixel with a normal distribution in CIE-Lab space estimated from its associated pixels. Global saliency is computed by grouping similar superpixels to estimate the spatial distribution of colors, while local saliency detection is achieved by determining the center-surround contrast of neighboring superpixels. Both methods rely on the Wasserstein distance on L2norm (W2) to measure perceptual (dis-)similarity between superpixels. Additionally, we propose a Saliency Flow technique to refine the local saliency map. Our approach uses very few empirical parameters and outperforms 6 recent state-of-the-art saliency detection methods in terms of several evaluations on a widely used benchmark.