72 Result(s)
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Article
Deep learning-based magnetic resonance image super-resolution: a survey
Magnetic resonance imaging (MRI) is a medical imaging technique used to show anatomical structures and physiological processes of the human body. Due to limitations like image acquisition time, hardware capabi...
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Article
Fragrant: frequency-auxiliary guided relational attention network for low-light action recognition
Video action recognition aims to classify actions within sequences of video frames, which has important applications in computer vision fields. Existing methods have shown proficiency in well-lit environments ...
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Chapter and Conference Paper
Adversarial Geometric Transformations of Point Clouds for Physical Attack
Towards adversarial physical attack in real world, we argue that the main challenge lies in discounting adversarial effects by changes of point density along object surface. Most of existing point-wise perturb...
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Chapter and Conference Paper
Sentiment Analysis Using Large Language Models: A Case Study of GPT-3.5
Sentiment analysis, which utilizes machine learning, natural language processing, and computational linguistics, has been developed to comprehend the emotions and viewpoints of individuals on social media plat...
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Article
Transformer model incorporating local graph semantic attention for image caption
Aiming at the problem of isolating semantic information of existing transformer-based models in the image captioning tasks, a transformer model incorporating local graph semantic attention (TLGSA) is proposed....
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Chapter and Conference Paper
Spatial-Temporal Electric Vehicle Charging Demand Forecasting: A GTrans Approach
Accurate forecasting of electric vehicle charging demand is vital for city planning and power system scheduling. Existing studies on this topic fall short in terms of exploiting the spatial locations of the ch...
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Chapter and Conference Paper
Predicting Learners’ Performance Using MOOC Clickstream
Massive Open Online Courses (MOOCs) have gradually become a dominant trend in online education. However, due to the large number of learners participating in MOOCs, teachers usually cannot accurately know the ...
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Chapter and Conference Paper
Knowledge-Enhanced Hierarchical Transformers for Emotion-Cause Pair Extraction
Emotion-cause pair extraction (ECPE) aims to extract all potential pairs of emotions and corresponding cause(s) from a given document. Current methods have focused on extracting possible emotion-cause pairs by...
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Article
Open AccessJoint self-supervised and reference-guided learning for depth inpainting
Depth information can benefit various computer vision tasks on both images and videos. However, depth maps may suffer from invalid values in many pixels, and also large holes. To improve such data, we propose ...
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Article
An improved method for sink node deployment in wireless sensor network to big data
Wireless sensor network (WSNs) technology and Internet technology penetrate and extend each other. It is a good way for physical changes of objects, state recognition and data collection, and becomes an import...
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Article
A joint model for entity and relation extraction based on BERT
In recent years, as the knowledge graph has attained significant achievements in many specific fields, which has become one of the core driving forces for the development of the internet and artificial intelli...
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Article
Hybrid neural network model for large-scale heterogeneous classification tasks in few-shot learning
How to generalize and unify different few-shot learning tasks using neural network model is a difficult problem in the field of machine learning research. Aiming at the problem that the parameters of existing ...
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Article
A topic-based multi-channel attention model under hybrid mode for image caption
Automatically generating captions of an image is not closely related to every spatial area of the visual information, but always related to the topic of the image expression. Aiming at the decoupling problem o...
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Chapter and Conference Paper
Category-Level 6D Object Pose and Size Estimation Using Self-supervised Deep Prior Deformation Networks
It is difficult to precisely annotate object instances and their semantics in 3D space, and as such, synthetic data are extensively used for these tasks, e.g., category-level 6D object pose and size estimation...
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Chapter and Conference Paper
Stochastic Consensus: Enhancing Semi-Supervised Learning with Consistency of Stochastic Classifiers
Semi-supervised learning (SSL) has achieved new progress recently with the emerging framework of self-training deep networks, where the criteria for selection of unlabeled samples with pseudo labels play a key...
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Chapter and Conference Paper
Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap
Semantic analyses of object point clouds are largely driven by releasing of benchmarking datasets, including synthetic ones whose instances are sampled from object CAD models. However, learning from synthetic ...
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Chapter and Conference Paper
DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation
Establishment of point correspondence between camera and object coordinate systems is a promising way to solve 6D object poses. However, surrogate objectives of correspondence learning in 3D space are a step a...
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Chapter and Conference Paper
A New Skeleton-Neural DAG Learning Approach
Learning a Directed Acyclic Graph (DAG) structure from observational data plays an essential role in causal inference and machine learning. A recent advance in the area is that the DAG learning problem was for...
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Chapter and Conference Paper
Human Control Intent Inference Using ESNs and Input-Tracking Based Inverse Model Predictive Control
Acquiring human motor control strategies or intents is helpful for clinical research, wearable robotic device design and human-robot cooperation control. The state-of-art method is to construct an optimal cont...
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Chapter and Conference Paper
Illuminate Low-Light Image via Coarse-to-fine Multi-level Network
Images under low light or against light are of low readability and visibility which in turn cause performance degradation of many computer vision tasks. As the crucial research objects for low-brightness image...
