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Visual tracking is a very important and challenging problem in the field of computer vision. In recent years, Siamese networks have been widely used for visual tracking due to their fast tracking speed, but many trackers based on Siamese network train their networks by utilizing either pairwise loss or triplet loss, which easily leads to over-fitting. In addition, it is difficult to distinguish some hard samples in the training samples. In this paper, we propose a novel global similarity loss to train the network.

Traditional matrix-based dimensional reduction methods, e.g., two-dimensional principal component analysis (2DPCA) and two-dimensional singular value decomposition (2DSVD), minimize mean square errors (MSE), which is sensitive to outliers. To overcome this problem, in this paper we propose a new robust 2DSVD method based on the kernel mean $p$ power error loss (KMPE-2DSVD).

Partial occlusions in face images pose a great problem for most face recognition algorithms due to the fact that most of these algorithms mainly focus on solving a second order loss function, e.g., mean square error (MSE), which will magnify the effect from occlusion parts. In this paper, we proposed a kernel non-second order loss function for sparse representation (KNS-SR) to recognize or restore partially occluded facial images, which both take the advantages of the correntropy and the non-second order statistics measurement.

Multi-scale object recognition and accurate object localization are two major problems for semantic segmentation in high resolution aerial images. To handle these problems, we design a Context Fuse Module to aggregate multi-scale features and propose an Attention Mix Module to combine different level features for higher localization accuracy. We further employ a Residual Convolutional Module to refine features in all levels. Based on these modules, we construct a new end-to-end network for semantic labeling in aerial images.

Automatic liver lesion classification on computed
tomography images is of great importance to early cancer
diagnosis and remains a challenging task. State-of-the-art
liver lesion classification algorithms are currently based on
manually selected regions of interest (ROIs) or automatically
detected ROIs. However, liver lesions usually vary in size
and shape, which makes the ROI selection process laborintensive
and also poses an obstacle to automatic lesion
detection. In this paper, we propose a dual-attention dilated

Automatic liver lesion classification on computed
tomography images is of great importance to early cancer
diagnosis and remains a challenging task. State-of-the-art
liver lesion classification algorithms are currently based on
manually selected regions of interest (ROIs) or automatically
detected ROIs. However, liver lesions usually vary in size
and shape, which makes the ROI selection process laborintensive
and also poses an obstacle to automatic lesion
detection. In this paper, we propose a dual-attention dilated

Automatic liver lesion classification on computed
tomography images is of great importance to early cancer
diagnosis and remains a challenging task. State-of-the-art
liver lesion classification algorithms are currently based on
manually selected regions of interest (ROIs) or automatically
detected ROIs. However, liver lesions usually vary in size
and shape, which makes the ROI selection process laborintensive
and also poses an obstacle to automatic lesion
detection. In this paper, we propose a dual-attention dilated

From video streaming to security and surveillance applications , video data play an important role in our daily living today. However, managing a large amount of video data and retrieving the most useful information for the user remain a challenging task. In this paper, we propose a novel video classification system that would benefit the scene understanding task. We define our classification problem as classifying background and foreground motions using the same feature representation for outdoor scenes.