Search Results for author:
Found 33 papers, 12 papers with code
Adaptive Graph Convolutional Network Framework for Multidimensional Time Series Prediction
In the real world, long sequence time-series forecasting (LSTF) is needed in many cases, such as power consumption prediction and air quality prediction. Multi-dimensional long time series model has more strict requirements on the model, which not only needs to effectively capture the accurate long-term dependence between input and output, but also needs to capture the relationship between data of different dimensions. Recent research shows that the Informer model based on Transformer has achieved excellent performance in long time series prediction. However, this model still has some deficiencies in multidimensional prediction, it cannot capture the relationship between different dimensions well.
M2R2: Missing-Modality Robust emotion Recognition framework with iterative data augmentation
This paper deals with the utterance-level modalities missing problem with uncertain patterns on emotion recognition in conversation (ERC) task.
Large-Scale Multi-Document Summarization with Information Extraction and Compression
We develop an abstractive summarization framework independent of labeled data for multiple heterogeneous documents.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
NAS-FCOS: Efficient Search for Object Detection Architectures
Neural Architecture Search (NAS) has shown great potential in effectively reducing manual effort in network design by automatically discovering optimal architectures.
Spatio-Temporal Interaction Graph Parsing Networks for Human-Object Interaction Recognition
With the effective spatio-temporal relationship modeling, it is possible not only to uncover contextual information in each frame but also to directly capture inter-time dependencies.
Joint Inductive and Transductive Learning for Video Object Segmentation
In this work, we propose to integrate transductive and inductive learning into a unified framework to exploit the complementarity between them for accurate and robust video object segmentation.
Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven Semi-supervised Method for Uncertainty Quantification
Based on the adaptive aPC, a semi-supervised deep adaptive arbitrary polynomial chaos expansion (Deep aPCE) method is proposed to reduce the training data cost and improve the surrogate model accuracy.
Diff-Net: Image Feature Difference based High-Definition Map Change Detection for Autonomous Driving
Compared to traditional methods based on object detectors, the essential design in our work is a parallel feature difference calculation structure that infers map changes by comparing features extracted from the camera and rasterized images.
CAT: Cross-Attention Transformer for One-Shot Object Detection
Given a query patch from a novel class, one-shot object detection aims to detect all instances of that class in a target image through the semantic similarity comparison.
Learning Models for Suicide Prediction from Social Media Posts
We propose a deep learning architecture and test three other machine learning models to automatically detect individuals that will attempt suicide within (1) 30 days and (2) six months, using their social media post data provided in the CLPsych 2021 shared task.
Data-Driven Optimized Tracking Control Heuristic for MIMO Structures: A Balance System Case Study
The heuristic decides on the control gains of multiple interacting PID control loops.
Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking
In video object tracking, there exist rich temporal contexts among successive frames, which have been largely overlooked in existing trackers.
Ranked #8 on
Visual Object Tracking
on LaSOT
Lattice reconstruction induced multiple ultra-flat bands in twisted bilayer WSe2
Moir\'e superlattices in van der Waals heterostructures provide a tunable platform to study emergent properties that are absent in the natural crystal form.
Mesoscale and Nanoscale Physics
Contrastive Transformation for Self-supervised Correspondence Learning
It is worth mentioning that our method also surpasses the fully-supervised affinity representation (e. g., ResNet) and performs competitively against the recent fully-supervised algorithms designed for the specific tasks (e. g., VOT and VOS).
Recurrent Feature Reasoning for Image Inpainting
To capture information from distant places in the feature map for RFR, we further develop KCA and incorporate it in RFR.
Unsupervised Deep Representation Learning for Real-Time Tracking
The advancement of visual tracking has continuously been brought by deep learning models.
Explainable Rumor Detection using Inter and Intra-feature Attention Networks
With social media becoming ubiquitous, information consumption from this media has also increased.
Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer's Disease
In this work, we propose three explainable deep learning architectures to automatically detect patients with Alzheimer`s disease based on their language abilities.
Cascaded Regression Tracking: Towards Online Hard Distractor Discrimination
In the second stage, a discrete sampling based ridge regression is designed to double-check the remaining ambiguous hard samples, which serves as an alternative of fully-connected layers and benefits from the closed-form solver for efficient learning.
Personalized Early Stage Alzheimer's Disease Detection: A Case Study of President Reagan's Speeches
In this paper, we show that machine learning-based unsupervised clustering of and anomaly detection with linguistic biomarkers are promising approaches for intuitive visualization and personalized early stage detection of Alzheimer`s disease.
Local Differential Privacy based Federated Learning for Internet of Things
To avoid the privacy threat and reduce the communication cost, in this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model.
The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform
We present the design and implementation of a visual search system for real time image retrieval on JD. com, the world's third largest and China's largest e-commerce site.
Real-Time Correlation Tracking via Joint Model Compression and Transfer
In the distillation process, we propose a fidelity loss to enable the student network to maintain the representation capability of the teacher network.
Collecting and Analyzing Multidimensional Data with Local Differential Privacy
Motivated by this, we first propose novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance.
Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding
It calculates emission score with similarity based methods and obtains transition score with a specially designed transfer mechanism.
NAS-FCOS: Fast Neural Architecture Search for Object Detection
The success of deep neural networks relies on significant architecture engineering.
Ranked #95 on
Object Detection
on COCO test-dev
Unsupervised Classification of Street Architectures Based on InfoGAN
Furthermore, we draw a series of heuristic conclusions from the intrinsic information hidden in true images.
Strain engineering of epitaxial oxide heterostructures beyond substrate limitations
The limitation of commercially available single-crystal substrates and the lack of continuous strain tunability preclude the ability to take full advantage of strain engineering for further exploring novel properties and exhaustively studying fundamental physics in complex oxides.
Materials Science
Multi-Cue Correlation Filters for Robust Visual Tracking
By combining different types of features, our approach constructs multiple experts through Discriminative Correlation Filter (DCF) and each of them tracks the target independently.
An Online Universal Classifier for Binary, Multi-class and Multi-label Classification
Several classifiers are developed for binary, multi-class and multi-label classification problems, but there are no classifiers available in the literature capable of performing all three types of classification.
A High Speed Multi-label Classifier based on Extreme Learning Machines
In this paper a high speed neural network classifier based on extreme learning machines for multi-label classification problem is proposed and dis-cussed.
