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Recently, black-box domain adaptation attracts a lot of attention. It is a new concept to realize domain adaptation of instance image classification with only a cloud API service, reflecting the focus on development of cloud services and concerns about data security. However, the existing black-box domain adaptation methods always only use high-confidence image samples which limits their classification performance. We propose a self-alignment approach to realize black-box domain adaptation. First, an initial model is constructed for target domain. Then, we put target data into source model API to obtain the pseudo-labels and divide the target data into high-confidence and low-confidence parts according to their pseudo-label confidences. By matching the data distributions between these two parts and self-supervised learning on high-confidence part, the classification performance of initial model on both parts samples can be boosted respectively. Information maximization is also applied to further improve classification performance. Experiment results confirm that our method achieves the state-of-the-art performance on image classification. [ABSTRACT FROM AUTHOR]

With the successful application of deformable convolution in aligning different video frames, it has also been used in video compression artifact reduction. The existing methods based on deformable convolution only apply 2D convolutional layers to generate the features for predicting alignment offsets, which is inaccurate due to limited receptive field. In this letter, we propose a new end-to-end network called Coarse-to-Fine Spatio-Temporal Information Fusion (CF-STIF) for compressed video quality enhancement by predicting better offsets with a larger receptive field. Specifically, several 3D convolutional layers are first to roughly fuse the spatio-temporal information in the video sequence, and then a Multi-level Residual Fusion Module (MLRF) is developed to generate global and local fused fine features from different levels for predicting deformable offsets. Thanks to the inherent advantages of 3D convolution and multi-scale strategy, the receptive field is greatly increased in both spatial and temporal dimensions, so that information from neighboring frames can be efficiently aggregated. In the end, the enhanced frame is derived by the proposed reconstruction module (REModule). Both qualitative and quantitative experimental results show that the proposed CF-STIF performs better than the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

A deep convolutional neural network (CNN) becomes a widely used tool for object detection. Many previous works have achieved excellent performance on object detection benchmarks. However, these works present generic detectors whose performance will drop rapidly when they are applied to a surveillance scene. In this paper, we propose an efficient method to construct a scene-specific regression model based on a generic CNN-based classifier. Our regression model is an adaptive deep CNN (ADCNN), which can predict object locations in the surveillance scene. First, we transfer the generic CNN-based classifier to the surveillance scene by selecting useful kernels. Second, we learn the context information of the surveillance scene in our regression model for accurate location prediction. Our main contributions are: 1) a transfer learning method that selects useful kernels in the generic CNN-based classifier; 2) a special architecture that can effectively learn the local and global context information in the surveillance scene; and 3) a new objective function to effectively train parameters in ADCNN. Compared with some state-of-the-art models, ADCNN achieves the best performance on three surveillance data sets for pedestrian detection and one surveillance data set for vehicle detection. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data. [ABSTRACT FROM PUBLISHER]

Moving object detection is about foreground and background separation based on motion detection. Detecting moving objects from similarly colored background (known as camouflage problem) has been a long-standing open question in this field. Discriminative modeling (DM), which focuses on enhancing the performance to distinguish foreground from background with discriminative features and well-designed classifiers, has been widely used for moving object detection. However, DM may tend to fail when encountering the camouflage problem, as the class separability in camouflaged areas is generally poor. In this paper, we propose a new strategy, camouflage modeling (CM), to identify camouflaged foreground pixels. In view of the fact that camouflage involves both foreground and background, we need to model both the background and the foreground, and compare them in a well-designed way in camouflage detection. Specifically, we develop a global model for the background, and an integration of global and local models for the foreground, respectively. Based on both background and foreground models, we introduce a factor to measure the degree of camouflage, and further identify truly camouflaged areas. In view of the fact that a moving object is usually composed of both camouflaged and noncamouflaged areas, CM and DM are fused in a Bayesian framework to perform complete object detection. Experiments are conducted on testing sequences to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]