Beyond Covariance: SICE and Kernel Based Visual Feature Representation

The past several years have witnessed increasing research interest on covariance-based feature representation. Originally proposed as a region descriptor, it has now been used as a general representation in various recognition tasks, demonstrating promising performance. However, covariance matrix has some inherent shortcomings such as singularity in the case of small sample, limited capability in modeling complicated feature relationship, and a single, fixed form of representation. To achieve better recognition performance, this paper argues that more capable and flexible symmetric positive definite (SPD)-matrix-based representation shall be explored, and this is attempted in this work by exploiting prior knowledge of data and nonlinear representation. Specifically, to better deal with the issues of small number of feature vectors and high feature dimensionality, we propose to exploit the structure sparsity of visual features and exemplify sparse inverse covariance estimate as a new feature representation. Furthermore, to effectively model complicated feature relationship, we propose to directly compute kernel matrix over feature dimensions, leading to a robust, flexible and open framework of SPD-matrix-based representation. Through theoretical analysis and experimental study, the proposed two representations well demonstrate their advantages over the covariance counterpart in skeletal human action recognition, image set classification and object classification tasks

A Self-boosting Framework for Automated Radiographic Report Generation

Automated radiographic report generation is a challenging task since it requires to generate paragraphs describing fine-grained visual differences of cases, especially for those between the diseased and the healthy. Existing image captioning methods commonly target at generic images, and lack mechanism to meet this requirement. To bridge this gap, in this paper, we propose a self-boosting framework that improves radiographic report generation based on the cooperation of the main task of report generation and an auxiliary task of image-text matching. The two tasks are built as the two branches of a network model and influence each other in a cooperative way. On one hand, the image-text matching branch helps to learn highly text-correlated visual features for the report generation branch to output high quality reports. On the other hand, the improved reports produced by the report generation branch provide additional harder samples for the image-text matching branch and enforce the latter to improve itself by learning better visual and text feature representations. This, in turn, helps improve the report generation branch again. These two branches are jointly trained to help improve each other iteratively and progressively, so that the whole model is self-boosted without requiring external resources. Experimental results demonstrate the effectiveness of our method on two public datasets, showing its superior performance over multiple state-of-the-art image captioning and medical report generation methods

RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning

In this work, we propose Reciprocal Distribution Alignment (RDA) to address semi-supervised learning (SSL), which is a hyperparameter-free framework that is independent of confidence threshold and works with both the matched (conventionally) and the mismatched class distributions. Distribution mismatch is an often overlooked but more general SSL scenario where the labeled and the unlabeled data do not fall into the identical class distribution. This may lead to the model not exploiting the labeled data reliably and drastically degrade the performance of SSL methods, which could not be rescued by the traditional distribution alignment. In RDA, we enforce a reciprocal alignment on the distributions of the predictions from two classifiers predicting pseudo-labels and complementary labels on the unlabeled data. These two distributions, carrying complementary information, could be utilized to regularize each other without any prior of class distribution. Moreover, we theoretically show that RDA maximizes the input-output mutual information. Our approach achieves promising performance in SSL under a variety of scenarios of mismatched distributions, as well as the conventional matched SSL setting. Our code is available at: https://github.com/NJUyued/RDA4RobustSSL

Table_3_Gut Microbiota Modulates the Protective Role of Ginsenoside Compound K Against Sodium Valproate-Induced Hepatotoxicity in Rat.DOCX

This study aimed to investigate the potential role of gut microbiota in the hepatotoxicity of sodium valproate (SVP) and the protective effect of ginsenoside compound K (G-CK) administration against SVP-induced hepatotoxicity in rats. Measurements of 16S rRNA showed that SVP supplementation led to a 140.749- and 248.900-fold increase in the relative abundance of Akkermansia muciniphila (A. muciniphila) and Bifidobacterium pseudolongum (B. pseudolongum), respectively (p 0.78, p 0.65, p < 0.01 or < 0.05). This alteration of the gut microbiota composition that resulted in observed changes to the glycolysis/gluconeogenesis and pyruvate metabolism may be involved in both the hepatotoxicity of SVP and the protective effect of G-CK administration against SVP-induced hepatotoxicity. Our study provides new evidence linking the gut microbiota with SVP-induced hepatotoxicity.</p

Table_2_Gut Microbiota Modulates the Protective Role of Ginsenoside Compound K Against Sodium Valproate-Induced Hepatotoxicity in Rat.DOCX

This study aimed to investigate the potential role of gut microbiota in the hepatotoxicity of sodium valproate (SVP) and the protective effect of ginsenoside compound K (G-CK) administration against SVP-induced hepatotoxicity in rats. Measurements of 16S rRNA showed that SVP supplementation led to a 140.749- and 248.900-fold increase in the relative abundance of Akkermansia muciniphila (A. muciniphila) and Bifidobacterium pseudolongum (B. pseudolongum), respectively (p 0.78, p 0.65, p < 0.01 or < 0.05). This alteration of the gut microbiota composition that resulted in observed changes to the glycolysis/gluconeogenesis and pyruvate metabolism may be involved in both the hepatotoxicity of SVP and the protective effect of G-CK administration against SVP-induced hepatotoxicity. Our study provides new evidence linking the gut microbiota with SVP-induced hepatotoxicity.</p

Number of ROIs in the hierarchy.

<p>Number of ROIs in the hierarchy.</p

Few-shot Unsupervised Domain Adaptation with Image-to-Class Sparse Similarity Encoding

This paper investigates a valuable setting called few-shot unsupervised domain adaptation (FS-UDA), which has not been sufficiently studied in the literature. In this setting, the source domain data are labelled, but with few-shot per category, while the target domain data are unlabelled. To address the FS-UDA setting, we develop a general UDA model to solve the following two key issues: the few-shot labeled data per category and the domain adaptation between support and query sets. Our model is general in that once trained it will be able to be applied to various FS-UDA tasks from the same source and target domains. Inspired by the recent local descriptor based few-shot learning (FSL), our general UDA model is fully built upon local descriptors (LDs) for image classification and domain adaptation. By proposing a novel concept called similarity patterns (SPs), our model not only effectively considers the spatial relationship of LDs that was ignored in previous FSL methods, but also makes the learned image similarity better serve the required domain alignment. Specifically, we propose a novel IMage-to-class sparse Similarity Encoding (IMSE) method. It learns SPs to extract the local discriminative information for classification and meanwhile aligns the covariance matrix of the SPs for domain adaptation. Also, domain adversarial training and multi-scale local feature matching are performed upon LDs. Extensive experiments conducted on a multi-domain benchmark dataset DomainNet demonstrates the state-of-the-art performance of our IMSE for the novel setting of FS-UDA. In addition, for FSL, our IMSE can also show better performance than most of recent FSL methods on miniImageNet

Table_6_Gut Microbiota Modulates the Protective Role of Ginsenoside Compound K Against Sodium Valproate-Induced Hepatotoxicity in Rat.DOCX

This study aimed to investigate the potential role of gut microbiota in the hepatotoxicity of sodium valproate (SVP) and the protective effect of ginsenoside compound K (G-CK) administration against SVP-induced hepatotoxicity in rats. Measurements of 16S rRNA showed that SVP supplementation led to a 140.749- and 248.900-fold increase in the relative abundance of Akkermansia muciniphila (A. muciniphila) and Bifidobacterium pseudolongum (B. pseudolongum), respectively (p 0.78, p 0.65, p < 0.01 or < 0.05). This alteration of the gut microbiota composition that resulted in observed changes to the glycolysis/gluconeogenesis and pyruvate metabolism may be involved in both the hepatotoxicity of SVP and the protective effect of G-CK administration against SVP-induced hepatotoxicity. Our study provides new evidence linking the gut microbiota with SVP-induced hepatotoxicity.</p

Selected discriminative features.

<p>Selected discriminative features.</p

Table_1_Gut Microbiota Modulates the Protective Role of Ginsenoside Compound K Against Sodium Valproate-Induced Hepatotoxicity in Rat.DOCX

This study aimed to investigate the potential role of gut microbiota in the hepatotoxicity of sodium valproate (SVP) and the protective effect of ginsenoside compound K (G-CK) administration against SVP-induced hepatotoxicity in rats. Measurements of 16S rRNA showed that SVP supplementation led to a 140.749- and 248.900-fold increase in the relative abundance of Akkermansia muciniphila (A. muciniphila) and Bifidobacterium pseudolongum (B. pseudolongum), respectively (p 0.78, p 0.65, p < 0.01 or < 0.05). This alteration of the gut microbiota composition that resulted in observed changes to the glycolysis/gluconeogenesis and pyruvate metabolism may be involved in both the hepatotoxicity of SVP and the protective effect of G-CK administration against SVP-induced hepatotoxicity. Our study provides new evidence linking the gut microbiota with SVP-induced hepatotoxicity.</p