A region-based image caption generator with refined descriptions

Describing the content of an image is a challenging task. To enable detailed description, it requires the detection and recognition of objects, people, relationships and associated attributes. Currently, the majority of the existing research relies on holistic techniques, which may lose details relating to important aspects in a scene. In order to deal with such a challenge, we propose a novel region-based deep learning architecture for image description generation. It employs a regional object detector, recurrent neural network (RNN)-based attribute prediction, and an encoder–decoder language generator embedded with two RNNs to produce refined and detailed descriptions of a given image. Most importantly, the proposed system focuses on a local based approach to further improve upon existing holistic methods, which relates specifically to image regions of people and objects in an image. Evaluated with the IAPR TC-12 dataset, the proposed system shows impressive performance and outperforms state-of-the-art methods using various evaluation metrics. In particular, the proposed system shows superiority over existing methods when dealing with cross-domain indoor scene images

Discriminative Block-Diagonal Representation Learning for Image Recognition

Existing block-diagonal representation studies mainly focuses on casting block-diagonal regularization on training data, while only little attention is dedicated to concurrently learning both block-diagonal representations of training and test data. In this paper, we propose a discriminative block-diagonal low-rank representation (BDLRR) method for recognition. In particular, the elaborate BDLRR is formulated as a joint optimization problem of shrinking the unfavorable representation from off-block-diagonal elements and strengthening the compact block-diagonal representation under the semisupervised framework of LRR. To this end, we first impose penalty constraints on the negative representation to eliminate the correlation between different classes such that the incoherence criterion of the extra-class representation is boosted. Moreover, a constructed subspace model is developed to enhance the self-expressive power of training samples and further build the representation bridge between the training and test samples, such that the coherence of the learned intraclass representation is consistently heightened. Finally, the resulting optimization problem is solved elegantly by employing an alternative optimization strategy, and a simple recognition algorithm on the learned representation is utilized for final prediction. Extensive experimental results demonstrate that the proposed method achieves superb recognition results on four face image data sets, three character data sets, and the 15 scene multicategories data set. It not only shows superior potential on image recognition but also outperforms the state-of-the-art methods

Arbitrary view action recognition via transfer dictionary learning on synthetic training data

Human action recognition is an important problem in robotic vision. Traditional recognition algorithms usually require the knowledge of view angle, which is not always available in robotic applications such as active vision. In this paper, we propose a new framework to recognize actions with arbitrary views. A main feature of our algorithm is that view-invariance is learned from synthetic 2D and 3D training data using transfer dictionary learning. This guarantees the availability of training data, and removes the hassle of obtaining real world video in specific viewing angles. The result of the process is a dictionary that can project real world 2D video into a view-invariant sparse representation. This facilitates the training of a view-invariant classifier. Experimental results on the IXMAS and N-UCLA datasets show significant improvements over existing algorithms

GII Representation-Based Cross-View Gait Recognition by Discriminative Projection With List-Wise Constraints

Remote person identification by gait is one of the most important topics in the field of computer vision and pattern recognition. However, gait recognition suffers severely from the appearance variance caused by the view change. It is very common that gait recognition has a high performance when the view is fixed but the performance will have a sharp decrease when the view variance becomes significant. Existing approaches have tried all kinds of strategies like tensor analysis or view transform models to slow down the trend of performance decrease but still have potential for further improvement. In this paper, a discriminative projection with list-wise constraints (DPLC) is proposed to deal with view variance in cross-view gait recognition, which has been further refined by introducing a rectification term to automatically capture the principal discriminative information. The DPLC with rectification (DPLCR) embeds list-wise relative similarity measurement among intraclass and inner-class individuals, which can learn a more discriminative and robust projection. Based on the original DPLCR, we have introduced the kernel trick to exploit nonlinear cross-view correlations and extended DPLCR to deal with the problem of multiview gait recognition. Moreover, a simple yet efficient gait representation, namely gait individuality image (GII), based on gait energy image is proposed, which could better capture the discriminative information for cross view gait recognition. Experiments have been conducted in the CASIA-B database and the experimental results demonstrate the outstanding performance of both the DPLCR framework and the new GII representation. It is shown that the DPLCR-based cross-view gait recognition has outperformed the-state-of-the-art approaches in almost all cases under large view variance. The combination of the GII representation and the DPLCR has further enhanced the performance to be a new benchmark for cross-view gait recognition

Chloroquine prevents acute kidney injury induced by lipopolysaccharide in rats via inhibition of inflammatory factors

Purpose: To investigate the role of chloroquine (CQ) in lipopolysaccharide (LPS)-induced renal injury in rats.Methods: Rats were assigned to one of four groups (n = 10). Control group was only given saline solution, whereas the model control, LPS + CQ, and LPS + yohimbine (YOH) + CQ groups were administered LPS intraperitoneally. At the end of the study, blood urea nitrogen (BUN) and creatinine (Cr) levels were determined.Results: CQ treatment significantly decreased the blood concentrations of tissue necrosis factor alpha (TNF-α), interleukin-6 (IL-6), IL-18, BUN, and Cr in the model control rats. There were also significant decreases in the levels of high mobility group protein 1 and kidney injury molecule-1 in the renal injury rats compared to the model control group. However, the inhibitory effects of CQ in the LPS-treated rats were blocked by treatment with YOH, an α-2-adrenergic receptor antagonist.Conclusions: Treatment with CQ attenuates LPS-induced renal injury by inhibiting inflammatory response.Keywords: Creatinine, Chloroquine, Inflammatory reactions, Kidney injury, Lipopolysaccharid

Ultra-low-frequency gravitational waves from individual supermassive black hole binaries as standard sirens

Ultra-low-frequency gravitational waves (GWs) generated by individual inspiraling supermassive black hole binaries (SMBHBs) in the centers of galaxies may be detected by pulsar timing arrays (PTAs) in the future. These GW signals encoding absolute cosmic distances can serve as bright and dark sirens, having potential to be developed into a precise cosmological probe. Here we show that an SKA-era PTA consisting of 100 millisecond pulsars may observe about 20 bright sirens and 90 dark sirens during a 10-year observation. The bright sirens, together with the CMB data, have comparable capabilities to current mainstream data for measuring the equation of state of dark energy. The dark sirens could make the measurement precision of the Hubble constant far beyond the standard of precision cosmology. Our results indicate that ultra-low-frequency GWs from individual SMBHBs are of great significance in exploring the nature of dark energy and measuring the Hubble constant.Comment: 42 pages, 10 figure