Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation

In this paper, we mainly focus on the problem of how to learn additional feature representations for few-shot image classification through pretext tasks (e.g., rotation or color permutation and so on). This additional knowledge generated by pretext tasks can further improve the performance of few-shot learning (FSL) as it differs from human-annotated supervision (i.e., class labels of FSL tasks). To solve this problem, we present a plug-in Hierarchical Tree Structure-aware (HTS) method, which not only learns the relationship of FSL and pretext tasks, but more importantly, can adaptively select and aggregate feature representations generated by pretext tasks to maximize the performance of FSL tasks. A hierarchical tree constructing component and a gated selection aggregating component is introduced to construct the tree structure and find richer transferable knowledge that can rapidly adapt to novel classes with a few labeled images. Extensive experiments show that our HTS can significantly enhance multiple few-shot methods to achieve new state-of-the-art performance on four benchmark datasets. The code is available at: https://github.com/remiMZ/HTS-ECCV22.Comment: 22 pages, 9 figures and 4 tables Accepted by ECCV 202

Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition

The purpose of few-shot recognition is to recognize novel categories with a limited number of labeled examples in each class. To encourage learning from a supplementary view, recent approaches have introduced auxiliary semantic modalities into effective metric-learning frameworks that aim to learn a feature similarity between training samples (support set) and test samples (query set). However, these approaches only augment the representations of samples with available semantics while ignoring the query set, which loses the potential for the improvement and may lead to a shift between the modalities combination and the pure-visual representation. In this paper, we devise an attributes-guided attention module (AGAM) to utilize human-annotated attributes and learn more discriminative features. This plug-and-play module enables visual contents and corresponding attributes to collectively focus on important channels and regions for the support set. And the feature selection is also achieved for query set with only visual information while the attributes are not available. Therefore, representations from both sets are improved in a fine-grained manner. Moreover, an attention alignment mechanism is proposed to distill knowledge from the guidance of attributes to the pure-visual branch for samples without attributes. Extensive experiments and analysis show that our proposed module can significantly improve simple metric-based approaches to achieve state-of-the-art performance on different datasets and settings.Comment: An expanded version of the same-name paper accepted by AAAI-202

Increasing Se concentration in maize grain with soil- or foliar-applied selenite on the Loess Plateau in China

AbstractSelenium (Se) is an essential mineral nutrient for animal and human growth. Deficiency in this element is a worldwide nutrition problem. Thus, this study determined the potential of increasing Se content in maize grain by using various Se fertiliser application techniques to improve the nutritional status of local residents. Field experiments were conducted on the Loess Plateau for two growing seasons to investigate the effects of different Se fertiliser application methods and application rates on the Se content in maize grain as well as the Se recovery, yield and status of other nutrients in maize grain under rain-fed conditions. Results show that soil and foliar Se applications exhibited no significant effects on maize biomass and grain yield as well as N, P, K, Ca, Mg, Fe, Mn, Cu and Zn contents in maize grain. However, both foliar and soil Se applications significantly improved the Se content in maize grain. Selenium content in maize grain is found to be linearly correlated with Se application rates, increasing from 0.12μgkg−1 to 0.33μgkg−1 by soil application at 1g of Se ha−1 and from 8.23μgkg−1 to 8.67μgkg−1 by foliar application at the same rate. Foliar application of Se showed higher Se recoveries in the grain compared with soil Se application: the former exhibited a maximum grain Se recovery rate of 52‰ and 106‰ in maize during the first and second growing seasons, respectively, whereas the latter was only 1.69‰ and 0.95‰, respectively. On the Loess Plateau in China, both soil and foliar Se applications effectively improved the Se content in maize grain. Compared with soil Se application, foliar Se application can improve the grain Se content in maize at reduced costs

Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning

Recent compositional zero-shot learning (CZSL) methods adapt pre-trained vision-language models (VLMs) by constructing trainable prompts only for composed state-object pairs. Relying on learning the joint representation of seen compositions, these methods ignore the explicit modeling of the state and object, thus limiting the exploitation of pre-trained knowledge and generalization to unseen compositions. With a particular focus on the universality of the solution, in this work, we propose a novel paradigm for CZSL models that establishes three identification branches (i.e., Multi-Path) to jointly model the state, object, and composition. The presented Troika is our implementation that aligns the branch-specific prompt representations with decomposed visual features. To calibrate the bias between semantically similar multi-modal representations, we further devise a Cross-Modal Traction module into Troika that shifts the prompt representation towards the current visual content. We conduct extensive experiments on three popular benchmarks, where our method significantly outperforms existing methods in both closed-world and open-world settings.Comment: 14 page

VGDiffZero: Text-to-image Diffusion Models Can Be Zero-shot Visual Grounders

Large-scale text-to-image diffusion models have shown impressive capabilities across various generative tasks, enabled by strong vision-language alignment obtained through pre-training. However, most vision-language discriminative tasks require extensive fine-tuning on carefully-labeled datasets to acquire such alignment, with great cost in time and computing resources. In this work, we explore directly applying a pre-trained generative diffusion model to the challenging discriminative task of visual grounding without any fine-tuning and additional training dataset. Specifically, we propose VGDiffZero, a simple yet effective zero-shot visual grounding framework based on text-to-image diffusion models. We also design a comprehensive region-scoring method considering both global and local contexts of each isolated proposal. Extensive experiments on RefCOCO, RefCOCO+, and RefCOCOg show that VGDiffZero achieves strong performance on zero-shot visual grounding

Sex differences in patients with COVID-19: a retrospective cohort study and meta-analysis

BACKGROUND: Accumulated evidence revealed that male was much more likely to higher severity and fatality by SARS-CoV-2 infection than female patients, but few studies and meta-analyses have evaluated the sex differences of the infection and progression of COVID-19 patients. AIM: We aimed to compare the sex differences of the epidemiological and clinical characteristics in COVID-19 patients; and to perform a meta-analysis evaluating the severe rate, fatality rate, and the sex differences of the infection and disease progression in COVID-19 patients. METHODS: We analyzed clinical data of patients in Changchun Infectious Hospital and Center, Changchun, Northeast China; and searched PubMed, Embase, Web of Science, and Cochrane Library without any language restrictions for published articles that reported the data of sex-disaggregated, number of severe, and death patients on the confirmed diagnosis of adult COVID-19 patients. RESULTS: The pooled severe rate and fatality rate of COVID-19 were 22.7% and 10.7%. Male incidence in the retrospective study was 58.1%, and the pooled incidence in male was 54.7%. CONCLUSION: The pooled severe rate in male and female of COVID-19 was 28.2% and 18.8%, the risky of severe and death was about 1.6folds higher in male compared with female, especially for older patients (> 50 y)

Altered Functional and Causal Connectivity of Cerebello-Cortical Circuits between Multiple System Atrophy (Parkinsonian Type) and Parkinson’s Disease

Lesions of the cerebellum lead to motor and non-motor deficits by influencing cerebral cortex activity via cerebello-cortical circuits. It remains unknown whether the cerebello-cortical “disconnection” underlies motor and non-motor impairments both in the parkinsonian variant of multiple system atrophy (MSA-P) and Parkinson’s disease (PD). In this study, we investigated both the functional and effective connectivity of the cerebello-cortical circuits from resting-state functional magnetic resonance imaging (rs-fMRI) data of three groups (26 MSA-P patients, 31 PD patients, and 30 controls). Correlation analysis was performed between the causal connectivity and clinical scores. PD patients showed a weakened cerebellar dentate nucleus (DN) functional coupling in the posterior cingulate cortex (PCC) and inferior parietal lobe compared with MSA-P or controls. MSA-P patients exhibited significantly enhanced effective connectivity from the DN to PCC compared with PD patients or controls, as well as declined causal connectivity from the left precentral gyrus to right DN compared with the controls, and this value is significantly correlated with the motor symptom scores. Our findings demonstrated a crucial role for the cerebello-cortical networks in both MSA-P and PD patients in addition to striatal-thalamo-cortical (STC) networks and indicated that different patterns of cerebello-cortical loop degeneration are involved in the development of the diseases

Association of Genetic Polymorphism of GSTM1 and GSTT1 with the Susceptibility to Antituberculosis Drug-induced Hepatotoxicity in Chinese Population

Objective To investigate the relationship between the polymorphism of glutathione S transferase M1, T1(GSTM1, GSTT1) gene and the susceptibility to antituberculosis drug induced hepatotoxicity (ATDH) in patients with tuberculosis. Methods GSTM1 and GSTT1 gene polymorphisms in patients with or without liver toxicity after antituberculous treatment were analyzed using multiple PCR method. Results In ATDH group and control group, the proportion of GSTM1 gene deletion was 58.0% and 50.7%respectively, and the difference was not statistically signifcant (OR=1.322, 95%CI=0.921~1.878), the frequencies of GSTT1 deletion were 46.3% and 49.3%, respectively, and there was no signifcant difference between them. There was no signifcant difference in frequency of GSTM1 and GSTT1 variation between case group and control group (P> 0.05), and no synergistic effect of those two gene polymorphism were detected in the occurrence of antituberculosis drug-induced hepatotoxicity. Conclusion The polymorphisms of GSTM1 and GSTT1 genes may not be associated with the risk of ATDH