Search Results for author:
Found 42 papers, 12 papers with code
SwG-former: A Sliding-Window Graph Convolutional Network for Simultaneous Spatial-Temporal Information Extraction in Sound Event Localization and Detection
Sound event localization and detection (SELD) involves sound event detection (SED) and direction of arrival (DoA) estimation tasks.
MEFLUT: Unsupervised 1D Lookup Tables for Multi-exposure Image Fusion
In this paper, we introduce a new approach for high-quality multi-exposure image fusion (MEF).
Application of Zone Method based Physics-Informed Neural Networks in Reheating Furnaces
Foundation Industries (FIs) constitute glass, metals, cement, ceramics, bulk chemicals, paper, steel, etc.
MidMed: Towards Mixed-Type Dialogues for Medical Consultation
To mitigate this challenge, we propose a novel task and create a human-to-human mixed-type medical consultation dialogue corpus, termed MidMed, covering five dialogue types: task-oriented dialogue for diagnosis, recommendation, knowledge-grounded dialogue, QA, and chitchat.
LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity
Motivated by it, we propose to use the local similarity (LocalSim) to learn node-level weighted fusion, which can also serve as a plug-and-play module.
Enhanced Soft Label for Semi-Supervised Semantic Segmentation
As a mainstream framework in the field of semi-supervised learning (SSL), self-training via pseudo labeling and its variants have witnessed impressive progress in semi-supervised semantic segmentation with the recent advance of deep neural networks.
Combinatorial optimization solving by coherent Ising machines based on spiking neural networks
Spiking neural network is a kind of neuromorphic computing that is believed to improve the level of intelligence and provide advantages for quantum computing.
On the Use of BERT for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation
In recent years, pre-trained models have become dominant in most natural language processing (NLP) tasks.
Ranked #1 on
Automated Essay Scoring
on ASAP
Diverse Message Passing for Attribute with Heterophily
Based on this attribute homophily rate, we propose a Diverse Message Passing (DMP) framework, which specifies every attribute propagation weight on each edge.
ASFlow: Unsupervised Optical Flow Learning with Adaptive Pyramid Sampling
Equipped with these two modules, our method achieves the best performance for unsupervised optical flow estimation on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015.
Motion Basis Learning for Unsupervised Deep Homography Estimation with Subspace Projection
Last, we propose a Feature Identity Loss (FIL) to enforce the learned image feature warp-equivariant, meaning that the result should be identical if the order of warp operation and feature extraction is swapped.
UPHDR-GAN: Generative Adversarial Network for High Dynamic Range Imaging with Unpaired Data
The ground truth images play a leading role in generating reasonable HDR images.
Integrated 3C in NOMA-enabled Remote-E-Health Systems
A novel framework is proposed to integrate communication, control and computing (3C) into the fifth-generation and beyond (5GB) wireless networks for satisfying the ultra-reliable low-latency connectivity requirements of remote-e-Health systems.
UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning
By integrating these two components together, our method achieves the best performance for unsupervised optical flow learning on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015.
Ranked #1 on
Optical Flow Estimation
on Sintel Final unsupervised
HypperSteer: Hypothetical Steering and Data Perturbation in Sequence Prediction with Deep Learning
Deep Recurrent Neural Networks (RNN) continues to find success in predictive decision-making with temporal event sequences.
OccInpFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning
Occlusion is an inevitable and critical problem in unsupervised optical flow learning.
FA-GANs: Facial Attractiveness Enhancement with Generative Adversarial Networks on Frontal Faces
To the best of our knowledge, we are the first to enhance the facial attractiveness with GANs in both geometry and appearance aspects.
Visual Summary of Value-level Feature Attribution in Prediction Classes with Recurrent Neural Networks
Deep Recurrent Neural Networks (RNN) is increasingly used in decision-making with temporal sequences.
DeepMeshFlow: Content Adaptive Mesh Deformation for Robust Image Registration
Deep homography methods, on the other hand, are free from such problem by learning deep features for robust performance.
Non-parametric Probabilistic Load Flow using Gaussian Process Learning
In this work, we propose a non-parametric probabilistic load flow (NP-PLF) technique based on the Gaussian Process (GP) learning to understand the power system behavior under uncertainty for better operational decisions.
Content-Aware Unsupervised Deep Homography Estimation
Homography estimation is a basic image alignment method in many applications.
Ranked #5 on
Homography Estimation
on S-COCO
Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques
We found that our proposed Ensemble CNN deep learning algorithms performed better for both classification tasks as compared to handcrafted machine learning algorithms, with 90% accuracy in ischaemia classification and 73% in infection classification.
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels
Specifically, we present an effective video saliency detector that consists of a spatial refinement network and a spatiotemporal module.
Ranked #1 on
Video Salient Object Detection
on VOS-T
(using extra training data)
Semi-supervised Skin Detection by Network with Mutual Guidance
In this paper we present a new data-driven method for robust skin detection from a single human portrait image.
The LAIX Systems in the BEA-2019 GEC Shared Task
In this paper, we describe two systems we developed for the three tracks we have participated in the BEA-2019 GEC Shared Task.
Ranked #16 on
Grammatical Error Correction
on BEA-2019 (test)
Frame-Recurrent Video Inpainting by Robust Optical Flow Inference
Compared with image inpainting, performing this task on video presents new challenges such as how to preserving temporal consistency and spatial details, as well as how to handle arbitrary input video size and length fast and efficiently.
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation
In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising.
Two-phase Hair Image Synthesis by Self-Enhancing Generative Model
Generating plausible hair image given limited guidance, such as sparse sketches or low-resolution image, has been made possible with the rise of Generative Adversarial Networks (GANs).
Visual Reasoning of Feature Attribution with Deep Recurrent Neural Networks
Deep Recurrent Neural Network (RNN) has gained popularity in many sequence classification tasks.
GIF2Video: Color Dequantization and Temporal Interpolation of GIF images
In this paper, we propose GIF2Video, the first learning-based method for enhancing the visual quality of GIFs in the wild.
MGANet: A Robust Model for Quality Enhancement of Compressed Video
In video compression, most of the existing deep learning approaches concentrate on the visual quality of a single frame, while ignoring the useful priors as well as the temporal information of adjacent frames.
A Simple but Effective Classification Model for Grammatical Error Correction
We treat grammatical error correction (GEC) as a classification problem in this study, where for different types of errors, a target word is identified, and the classifier predicts the correct word form from a set of possible choices.
Video Inpainting by Jointly Learning Temporal Structure and Spatial Details
We present a new data-driven video inpainting method for recovering missing regions of video frames.
Transition-Based Chinese AMR Parsing
This paper presents the first AMR parser built on the Chinese AMR bank.
Getting the Most out of AMR Parsing
This paper proposes to tackle the AMR parsing bottleneck by improving two components of an AMR parser: concept identification and alignment.
Ranked #7 on
AMR Parsing
on LDC2014T12
Addressing the Data Sparsity Issue in Neural AMR Parsing
Neural attention models have achieved great success in different NLP tasks.
Look, Listen and Learn - A Multimodal LSTM for Speaker Identification
This task not only requires collective perception over both visual and auditory signals, the robustness to handle severe quality degradations and unconstrained content variations are also indispensable.
