Search Results for author:
Found 17 papers, 8 papers with code
Sim2Real in Reconstructive Spectroscopy: Deep Learning with Augmented Device-Informed Data Simulation
This work proposes a deep learning (DL)-based framework, namely Sim2Real, for spectral signal reconstruction in reconstructive spectroscopy, focusing on efficient data sampling and fast inference time.
Neural Collapse in Multi-label Learning with Pick-all-label Loss
We study deep neural networks for the multi-label classification (MLab) task through the lens of neural collapse (NC).
FastInst: A Simple Query-Based Model for Real-Time Instance Segmentation
In this paper, we show the strong potential of query-based models on efficient instance segmentation algorithm designs.
Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection
End-to-end pedestrian detection focuses on training a pedestrian detection model via discarding the Non-Maximum Suppression (NMS) post-processing.
BioNet: A Biologically-Inspired Network for Face Recognition
To show our idea experimentally, we model the biological characteristics of the human face-recognizing system with classical Convolutional Neural Network Operators (CNN Ops) purposely.
Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing
Along with the widespread use of face recognition systems, their vulnerability has become highlighted.
LongShortNet: Exploring Temporal and Semantic Features Fusion in Streaming Perception
Streaming perception is a critical task in autonomous driving that requires balancing the latency and accuracy of the autopilot system.
Matrix Completion with Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR Sampling
While uniform sampling has been widely studied in the matrix completion literature, CUR sampling approximates a low-rank matrix via row and column samples.
Dense Learning based Semi-Supervised Object Detection
Semi-supervised object detection (SSOD) aims to facilitate the training and deployment of object detectors with the help of a large amount of unlabeled data.
Guided Semi-Supervised Non-negative Matrix Factorization on Legal Documents
Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets.
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting
In crowd counting, due to the problem of laborious labelling, it is perceived intractability of collecting a new large-scale dataset which has plentiful images with large diversity in density, scene, etc.
Virtual Fully-Connected Layer: Training a Large-Scale Face Recognition Dataset With Limited Computational Resources
This is because the classification paradigm needs to train a fully connected layer as the category classifier, and its parameters will be in the hundreds of millions if the training dataset contains millions of identities.
VirFace: Enhancing Face Recognition via Unlabeled Shallow Data
In this paper, we propose a novel face recognition method, named VirFace, to effectively apply the unlabeled shallow data for face recognition.
Analysis of Legal Documents via Non-negative Matrix Factorization Methods
The California Innocence Project (CIP), a clinical law school program aiming to free wrongfully convicted prisoners, evaluates thousands of mails containing new requests for assistance and corresponding case files.
COVID-19 Literature Topic-Based Search via Hierarchical NMF
A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available.
NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit
In this paper, we introduce NeuralClassifier, a toolkit for neural hierarchical multi-label text classification.
General Classification
Hierarchical Multi-label Classification
+4
Logical Parsing from Natural Language Based on a Neural Translation Model
Under our learning policy, the Seq2Seq model can learn mappings gradually with noises.
