A5009822141- Multimodal Machine Learning Applications
- Natural Language Processing Techniques
- Topic Modeling
- Domain Adaptation and Few-Shot Learning
- Advanced Neural Network Applications
- Human Pose and Action Recognition
- Advanced Text Analysis Techniques
- Advanced Image and Video Retrieval Techniques
- Syntax, Semantics, Linguistic Variation
- Data Visualization and Analytics
- Music and Audio Processing
- Video Surveillance and Tracking Methods
- Sentiment Analysis and Opinion Mining
- Sparse and Compressive Sensing Techniques
- Critical Realism in Sociology
- Authorship Attribution and Profiling
- Metabolism and Genetic Disorders
- Topological and Geometric Data Analysis
- Handwritten Text Recognition Techniques
- Humor Studies and Applications
- Psychoanalysis, Philosophy, and Politics
- Machine Learning and ELM
- Generative Adversarial Networks and Image Synthesis
- Alcoholism and Thiamine Deficiency
- Access Control and Trust
Cancer Hospital of Chinese Academy of Medical Sciences
2025
Ocean University of China
2022-2024
Institute of Biophysics
2024
Chinese Academy of Sciences
2016-2024
Sun Yat-sen University
2023
University of Chinese Academy of Sciences
2020-2022
Aerospace Information Research Institute
2020-2022
Institute of Electronics
2022
Microsoft Research (United Kingdom)
2021
Zhejiang University
2021
Much of the recent work in remote sensing image captioning is influenced by natural captioning. These methods tend to fix defects model architecture improve previous work, but pay little attention differences between images and images. By considering these differences, we propose a multiscale multiinteraction model. As Fig. 1(a), targets have wide range scales; while are generally taken close-up, resulting similar scale for foreground targets. Due difference shooting methods, pretrained on...
Leveraging lexical constraint is extremely significant in domain-specific machine translation and interactive translation. Previous studies mainly focus on extending beam search algorithm or augmenting the training corpus by replacing source phrases with corresponding target These methods either suffer from heavy computation cost during inference depend quality of bilingual dictionary pre-specified user constructed statistical In response to these problems, we present a conceptually simple...
Abstract As a subfield of deep learning (DL), generative adversarial networks (GANs) have produced impressive results by applying models to create synthetic data and performing an training process. Nevertheless, numerous issues related the instability need be urgently addressed. Evolutionary computation (EC), using corresponding paradigm biological evolution, overcomes these problems improves evolutionary-based GANs’ ability deal with real-world applications. Therefore, this paper presents...
<title>Abstract</title> Foundational models have emerged as powerful tools for addressing various tasks in clinical settings. However, their potential development to breast ultrasound analysis remains untapped. In this paper, we present BUSGen, the first foundational generative model specifically designed image analysis. Pretrained on over 3.5 million images, BUSGen has acquired extensive knowledge of structures, pathological features, and variations. With few-shot adaptation, can generate...
In this article I first examine the ways in which dual terms of structure and agency are used sociological theories. Then, relying on Lacan’s notions split‐subject, formula sexuation, forms discourses, Laclau’s theory ideological hegemony, argue that most current formulations is but a posited other dissolves if examined closely; it similar to Lacanian fantasmic object. To resolve fundamental paradoxes structure‐agency theories, reformulate structures as paradoxical, incomplete, contingent...
It is widely shared that capturing relationships among multi-modality features would be helpful for representing and ultimately describing an image. In this paper, we present a novel Intra- Inter-modality visual Relation Transformer to improve connections features, termed I2RT. Firstly, propose Enhanced Block (RETB) image feature learning, which strengthens intra-modality relations objects. Moreover, bridge the gap between inter-modality representations, align them explicitly via Visual...
Presents a collection of slides covering the following: software architecture; Big Data; data analysis; and SQL.
Modern neural machine translation (NMT) models employ a large number of parameters, which leads to serious over-parameterization and typically causes the underutilization computational resources. In response this problem, we empirically investigate whether redundant parameters can be reused achieve better performance. Experiments analyses are systematically conducted on different datasets NMT architectures. We show that: 1) pruned rejuvenated improve baseline model by up +0.8 BLEU points; 2)...
Thiamine and pyridoxine are essential B vitamins that serve as enzymatic cofactors in energy metabolism, protein nucleic acid biosynthesis, neurotransmitter production. In humans, thiamine transporters SLC19A2 SLC19A3 primarily regulate cellular uptake of both vitamins. Genetic mutations these transporters, which cause deficiency, have been implicated severe neurometabolic diseases. Additionally, various prescribed medicines, including metformin fedratinib, manipulate complicating the...
In multi-view learning applications, like multimedia analysis and information retrieval, we often encounter the corrupted view problem in which data are by two different types of noises, i.e., intra- inter-view noises. The noises may affect these applications that commonly acquire complementary representations from views. Therefore, how to denoise views is great importance for integrate analyze However, heterogeneity among brings a significant challenge on denoising To address this...
Graph Neural Networks (GNNs) aim to extend deep learning techniques graph data and have achieved significant progress in analysis tasks (e.g., node classification) recent years. However, similar other neural networks like Convolutional (CNNs) Recurrent (RNNs), GNNs behave a black box with their details hidden from model developers users. It is therefore difficult diagnose possible errors of GNNs. Despite many visual analytics studies being done on CNNs RNNs, little research has addressed the...
Abstract Aiming at the current situation that deep learning has a strong dependence on sufficient samples and it is difficult to obtain labeled in practical applications, few-shot image classification method combining attention mechanism proposed. Firstly, feature extraction module adds an increase weight of important parts, pay more parts image, corresponding vector. Secondly, relevance measurement used calculate similarity between features determine category query image. Experiments have...
Data-driven deep learning models have shown great capabilities to assist radiologists in breast ultrasound (US) diagnoses. However, their effectiveness is limited by the long-tail distribution of training data, which leads inaccuracies rare cases. In this study, we address a long-standing challenge improving diagnostic model performance on cases using long-tailed data. Specifically, introduce pipeline, TAILOR, that builds knowledge-driven generative produce tailored synthetic The model,...
This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension Abstract Meaning (ReCAM). We explain algorithms used to learn models and process tuning selecting best model. Inspired by similarity ReCAM task language pre-training, we propose a simple yet effective technology, namely, negative augmentation with Evaluation results demonstrate effectiveness proposed approach. Our achieve 4th rank on both official test sets Subtask 1 2 an accuracy 87.9% 92.8%,...
In order to reflect the dynamic feature of situations and peers' behavior more accurately, context information, which contains different attributes situations, should be considered explicitly when evaluating trust. A context-aware trust establishment framework based on Bayesian networks is proposed. our proposal, contextual factors are inferred statistically, not assigned in ad hoc way as most existing solutions. We also propose a mapping approach between related contexts help application...
Multi-spectral pedestrian detection has been proven to overcome the limitations of visible-modal by using both visible and thermal images, such as low light, cluttered background, thus enabling all-day detection. However, fusion strategy, which directly uses feature addition, cannot make full use multi-spectral image information, even interferes with results. In this paper, consistency module is introduced guide features different modalities map into same space, so minimize differences...