Search Results for author:
Found 24 papers, 10 papers with code
Semi-supervised Counting via Pixel-by-pixel Density Distribution Modelling
This paper focuses on semi-supervised crowd counting, where only a small portion of the training data are labeled.
Gramformer: Learning Crowd Counting via Graph-Modulated Transformer
The graph is building upon the dissimilarities between patches, modulating the attention in an anti-similarity fashion.
YOLO-Angio: An Algorithm for Coronary Anatomy Segmentation
Coronary angiography remains the gold standard for diagnosis of coronary artery disease, the most common cause of death worldwide.
Ranked #2 on
Coronary Artery Segmentation
on ARCADE
StenUNet: Automatic Stenosis Detection from X-ray Coronary Angiography
Coronary angiography continues to serve as the primary method for diagnosing coronary artery disease (CAD), which is the leading global cause of mortality.
Ranked #2 on
Stenosis Segmentation
on ARCADE
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data
To address this issue, this paper presents a cross-silo prototypical calibration method (FedCSPC), which takes additional prototype information from the clients to learn a unified feature space on the server side.
LGViT: Dynamic Early Exiting for Accelerating Vision Transformer
Recently, the efficient deployment and acceleration of powerful vision transformers (ViTs) on resource-limited edge devices for providing multimedia services have become attractive tasks.
Metric Learning-Based Timing Synchronization by Using Lightweight Neural Network
However, multi-path uncertainty corrupts the TS correctness, making OFDM systems suffer from a severe inter-symbol-interference (ISI).
Dance of SNN and ANN: Solving binding problem by combining spike timing and reconstructive attention
In this paper, we propose a brain-inspired hybrid neural network (HNN) that introduces temporal binding theory originated from neuroscience into ANNs by integrating spike timing dynamics (via spiking neural networks, SNNs) with reconstructive attention (by ANNs).
Semi-supervised Crowd Counting via Density Agency
In this paper, we propose a new agency-guided semi-supervised counting approach.
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
Boosting Crowd Counting via Multifaceted Attention
Secondly, we design the Local Attention Regularization to supervise the training of LRA by minimizing the deviation among the attention for different feature locations.
Object Counting: You Only Need to Look at One
This paper aims to tackle the challenging task of one-shot object counting.
Ranked #8 on
Object Counting
on FSC147
Direct Measure Matching for Crowd Counting
Second, we derive a semi-balanced form of Sinkhorn divergence, based on which a Sinkhorn counting loss is designed for measure matching.
Attention-based Transducer for Online Speech Recognition
We propose attention-based transducer with modification over RNN-T in two aspects.
Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection
Off-topic spoken response detection, the task aiming at predicting whether a response is off-topic for the corresponding prompt, is important for an automated speaking assessment system.
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)
Give Me More Feedback II: Annotating Thesis Strength and Related Attributes in Student Essays
While the vast majority of existing work on automated essay scoring has focused on holistic scoring, researchers have recently begun work on scoring specific dimensions of essay quality.
SDN-based In-network Honeypot: Preemptively Disrupt and Mislead Attacks in IoT Networks
Detecting cyber attacks in the network environments used by Internet-of-things (IoT) and preventing them from causing physical perturbations play an important role in delivering dependable services.
Cryptography and Security Networking and Internet Architecture
Attention-based sequence-to-sequence model for speech recognition: development of state-of-the-art system on LibriSpeech and its application to non-native English
Recent research has shown that attention-based sequence-to-sequence models such as Listen, Attend, and Spell (LAS) yield comparable results to state-of-the-art ASR systems on various tasks.
Hybrid CTC-Attention based End-to-End Speech Recognition using Subword Units
In this paper, we present an end-to-end automatic speech recognition system, which successfully employs subword units in a hybrid CTC-Attention based system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
Indoor Space Recognition using Deep Convolutional Neural Network: A Case Study at MIT Campus
In this paper, we propose a robust and parsimonious approach using Deep Convolutional Neural Network (DCNN) to recognize and interpret interior space.
A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization
In the period since 2004, many novel sophisticated approaches for generic multi-document summarization have been developed.
On fast approximate submodular minimization
We are motivated by an application to extract a representative subset of machine learning training data and by the poor empirical performance we observe of the popular minimum norm algorithm.
