Search Results for author:
Found 27 papers, 11 papers with code
Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information
Recently, zero-shot entity linking task has become a research hotspot, which links mentions to unseen entities to challenge the generalization ability.
Acknowledgement Entity Recognition in CORD-19 Papers
We built a supplementary database by linking CORD-19 papers with acknowledgement entities extracted by AckExtract including persons and organizations and find that only up to 50–60% of named entities are actually acknowledged.
ViKL: A Mammography Interpretation Framework via Multimodal Aggregation of Visual-knowledge-linguistic Features
Based on this dataset, we focus on the challanging task of unsupervised pretraining and propose ViKL, a innovative framework that synergizes Visual, Knowledge, and Linguistic features.
ManiNeg: Manifestation-guided Multimodal Pretraining for Mammography Classification
Breast cancer is a significant threat to human health.
Movable-Antenna Position Optimization for Physical-Layer Security via Discrete Sampling
To tackle this challenge, we ingeniously transform this combinatory problem into a recursive path selection problem in graph theory and propose a partial enumeration algorithm to obtain its optimal solution without the need for high-complexity exhaustive search.
Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning
Forensic pathology is critical in determining the cause and manner of death through post-mortem examinations, both macroscopic and microscopic.
Joint Beamforming and Antenna Position Optimization for Movable Antenna-Assisted Spectrum Sharing
However, such an optimization problem is difficult to be optimally solved due to the highly nonlinear functions of the received signal/interference power at the SR/all PRs in terms of the MA positions.
Ensemble Learning to Assess Dynamics of Affective Experience Ratings and Physiological Change
The congruence between affective experiences and physiological changes has been a debated topic for centuries.
DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing Understanding
CV tasks, such as image captioning, which has primarily been carried out on natural images, still struggle to produce accurate and meaningful captions on sketched images often included in scientific and technical documents.
Towards Accurate Acne Detection via Decoupled Sequential Detection Head
In addition, we build a high-quality acne detection dataset named ACNE-DET to verify the effectiveness of DSDH.
Decreased serum vitamin D level as a prognostic marker in patients with COVID-19
ROC curve showed the serum vitamin D level could predict the severity classification and prognosis of COVID-19 significantly. Conclusions: Serum 25(OH)D3 level is independently associated with the severity of COVID-19 in elderly, and it could be used as a predictor of the severity of COVID-19.
Spiral Contrastive Learning: An Efficient 3D Representation Learning Method for Unannotated CT Lesions
In the past five years, several works have tailored for unsupervised representations of CT lesions via two-dimensional (2D) and three-dimensional (3D) self-supervised learning (SSL) algorithms.
Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning
Long-term vertebral fractures severely affect the life quality of patients, causing kyphotic, lumbar deformity and even paralysis.
Learning High-quality Proposals for Acne Detection
Acne detection is crucial for interpretative diagnosis and precise treatment of skin disease.
Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension
Procedural Multimodal Documents (PMDs) organize textual instructions and corresponding images step by step.
A Synthetic Prediction Market for Estimating Confidence in Published Work
Explainably estimating confidence in published scholarly work offers opportunity for faster and more robust scientific progress.
Learning Canonical View Representation for 3D Shape Recognition with Arbitrary Views
In this way, each 3D shape with arbitrary views is represented by a fixed number of canonical view features, which are further aggregated to generate a rich and robust 3D shape representation for shape recognition.
GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Images
To model the relationship, we propose the Graph Regularized Embedding Network (GREN), which leverages the intra-image and inter-image information to locate diseases on chest X-ray images.
Entropy-based Optimization via A* Algorithm for Parking Space Recommendation
This paper addresses the path planning problems for recommending parking spaces, given the difficulties of identifying the most optimal route to vacant parking spaces and the shortest time to leave the parking space.
Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models
In this paper, we investigate prediction of the reproducibility of SBS papers using machine learning methods based on a set of features.
Deep Positional and Relational Feature Learning for Rotation-Invariant Point Cloud Analysis
In this work, we propose a novel deep network for point clouds by incorporating positional information of points as inputs while yielding rotation-invariance.
GMH: A General Multi-hop Reasoning Model for KG Completion
To the best of our knowledge, there lacks a general framework that approaches multi-hop reasoning in mixed long-short distance reasoning scenarios.
Object-based Illumination Estimation with Rendering-aware Neural Networks
With the estimated lighting, virtual objects can be rendered in AR scenarios with shading that is consistent to the real scene, leading to improved realism.
View-GCN: View-Based Graph Convolutional Network for 3D Shape Analysis
View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition.
HRGE-Net: Hierarchical Relational Graph Embedding Network for Multi-view 3D Shape Recognition
We construct a relational graph with multi-view images as nodes, and design relational graph embedding by modeling pairwise and neighboring relations among views.
Tetris: Re-architecting Convolutional Neural Network Computation for Machine Learning Accelerators
Inference efficiency is the predominant consideration in designing deep learning accelerators.
Minimum Margin Loss for Deep Face Recognition
Face recognition has achieved great progress owing to the fast development of the deep neural network in the past a few years.
