492 Result(s)
within
Conference Paper
**li Meng
Computer Science
Artificial Intelligence
Image Processing and Computer Vision
-
Chapter and Conference Paper
Correlation Analysis Between Insomnia Severity and Depressive Symptoms of College Students Based on Pseudo-Siamese Network
To explore the correlation between emotional mood and sleep quality in a college student population, we propose a new method based on pseudo-siamese network, which can quickly diagnose the causes of depression...
-
Chapter and Conference Paper
Task-Adaptive Generative Adversarial Network Based Speech Dereverberation for Robust Speech Recognition
Reverberation is known to severely affect speech recognition performance when speech is recorded in an enclosed space. Deep learning-based speech dereverberation has been remarkably successful in recent years,...
-
Chapter and Conference Paper
Deployment and Comparison of Large Language Models Based on Virtual Cluster
[Objective] Currently, large language model (LLM) is one of research highlights in the field of natural language processing. This paper selected some open-source LLMs for deployment and comparison from the perspe...
-
Chapter and Conference Paper
GIST: Transforming Overwhelming Information into Structured Knowledge with Large Language Models
This paper introduces GIST (Generative Information Synthesis Taskforce), a novel personal knowledge management system that utilizes large-scale online language models to analyze and organize the information, g...
-
Chapter and Conference Paper
Integrating Human Parsing and Pose Network for Human Action Recognition
Human skeletons and RGB sequences are both widely-adopted input modalities for human action recognition. However, skeletons lack appearance features and color data suffer large amount of irrelevant depiction. ...
-
Chapter and Conference Paper
Updates and Experiences of VenusAI Platform
[Objective] This paper presents an overview introduction of the VenusAI platform, focusing on its technical updates and sharing the experiences gained since its deployment. The objective is to highlight the platf...
-
Chapter and Conference Paper
FAI: A Fraudulent Account Identification System
Fraudulent account detection is essential for businesses and online Internet enterprises, which can help to avoid financial loss and improve user experience. However, conventional solutions suffer from two mai...
-
Chapter and Conference Paper
A Semantic Genetic Programming Approach to Evolving Heuristics for Multi-objective Dynamic Scheduling
Multi-objective dynamic flexible job shop scheduling (MO-DFJSS) is a challenging problem that requires finding high-quality schedules for jobs in a dynamic and flexible manufacturing environment, considering m...
-
Chapter and Conference Paper
Foreground and Background Separate Adaptive Equilibrium Gradients Loss for Long-Tail Object Detection
The current mainstream object detection methods usually tend to implement on datasets where the categories remain balanced, and have made great progress. However, in the presence of long-tail distribution, the...
-
Chapter and Conference Paper
Unsupervised Segmentation of Haze Regions as Hard Attention for Haze Classification
Haze classification plays a crucial role in air quality and visibility assessment. In contrast to traditional image classification, haze classification requires the classifier to capture the characteristics of...
-
Chapter and Conference Paper
Learning to Fuse Residual and Conditional Information for Video Compression and Reconstruction
With the rapid development of the Internet, video compression and reconstruction have attracted more and more attention as the use and transmission frequency of video data have increased dramatically. Traditio...
-
Chapter and Conference Paper
TTA-GCN: Temporal Topology Aggregation for Skeleton-Based Action Recognition
Graph Convolutional Networks (GCNs) have been widely used in skeleton-based action recognition. In GCN-based approaches, graph topology dominates feature aggregation, and therefore extraction of the complex re...
-
Chapter and Conference Paper
Brain Tumor Sequence Registration with Non-iterative Coarse-To-Fine Networks and Dual Deep Supervision
In this study, we focus on brain tumor sequence registration between pre-operative and follow-up Magnetic Resonance Imaging (MRI) scans of brain glioma patients, in the context of Brain Tumor Sequence Registra...
-
Chapter and Conference Paper
Research on Emotional Classification and Literary Narrative Visualization Based on Graph Convolutional Neural Network
Watching a movie for three minutes has become a popular term in contemporary life, and people also hope to spend less time understanding the emotional direction and general plot of long novels. Currently, rese...
-
Chapter and Conference Paper
GAN-Based Image Compression with Improved RDO Process
GAN-based image compression schemes have shown remarkable progress lately due to their high perceptual quality at low bit rates. However, there are two main issues, including 1) the reconstructed image percept...
-
Chapter and Conference Paper
Towards Automated Segmentation of Human Abdominal Aorta and Its Branches Using a Hybrid Feature Extraction Module with LSTM
Abdominal aortic aneurysm (AAA) is a disease with high rates of morbidity and mortality. For AAA treatment, appropriately covered stents are placed to prevent blood from entering the aneurysm. Before the inter...
-
Chapter and Conference Paper
A Full-Level Based Network to Detect Every Aircraft in Airport Scene
The rapid development of civil aviation has led to increasingly crowded airports. The complex airport environment and large number of aircraft make airport object detection a difficult task. Due to the large e...
-
Chapter and Conference Paper
CFAB: An Online Data Augmentation to Alleviate the Spuriousness of Classification on Medical Ultrasound Images
Convolutional neural networks (CNNs) may learn spurious correlations between bias features (e.g., background) and labels in image classification. The spuriousness in CNNs usually occurs in building connections be...
-
Chapter and Conference Paper
Improving Adversarial Robustness via Channel and Depth Compatibility
Several deep neural networks are vulnerable to adversarial samples that are imperceptible to humans. To address this challenge, a range of techniques have been proposed to design more robust model architecture...
-
Chapter and Conference Paper
FastFoley: Non-autoregressive Foley Sound Generation Based on Visual Semantics
Foley sound in movies and TV episodes is of great importance to bring a more realistic feeling to the audience. Traditionally, foley artists need to create the foley sound synchronous with the content occurrin...
