Search Results for author:
Found 40 papers, 17 papers with code
FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models
In this paper, we propose FedRSCLIP, the first federated learning framework designed for remote sensing image classification based on a VLM, specifically CLIP.
Generalization-Enhanced Few-Shot Object Detection in Remote Sensing
However, the generalization capability of FSOD models, particularly in remote sensing, is often constrained by the complex and diverse characteristics of the objects present in such environments.
Rapid Reconstruction of Extremely Accelerated Liver 4D MRI via Chained Iterative Refinement
The inference time of CIRNet, CS, and Re-Con-GAN are 11s, 120s, and 0. 15s.
Structural Entropy Guided Probabilistic Coding
Probabilistic embeddings have several advantages over deterministic embeddings as they map each data point to a distribution, which better describes the uncertainty and complexity of data.
RS-MoE: Mixture of Experts for Remote Sensing Image Captioning and Visual Question Answering
We fine-tuned our model on the RSICap dataset using our proposed training strategy.
Zero-shot Object Navigation with Vision-Language Models Reasoning
Zero-shot object navigation (ZSON) aims to address this challenge, allowing robots to interact with unknown objects without specific training data.
DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector
Graph Anomaly Detection (GAD) is crucial for identifying abnormal entities within networks, garnering significant attention across various fields.
MECD: Unlocking Multi-Event Causal Discovery in Video Reasoning
Video causal reasoning aims to achieve a high-level understanding of video content from a causal perspective.
Ranked #1 on
Causal Discovery in Video Reasoning
on MECD
DRL-STNet: Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation via Disentangled Representation Learning
Unsupervised domain adaptation (UDA) is essential for medical image segmentation, especially in cross-modality data scenarios.
Customizing Language Models with Instance-wise LoRA for Sequential Recommendation
Sequential recommendation systems predict the next interaction item based on users' past interactions, aligning recommendations with individual preferences.
Counterfactual Debating with Preset Stances for Hallucination Elimination of LLMs
CFMAD presets the stances of LLMs to override their inherent biases by compelling LLMs to generate justifications for a predetermined answer's correctness.
Paired Conditional Generative Adversarial Network for Highly Accelerated Liver 4D MRI
Re-Con-GAN processed the data (3D+t) as temporal slices (2D+t).
Airport Delay Prediction with Temporal Fusion Transformers
Since flight delay hurts passengers, airlines, and airports, its prediction becomes crucial for the decision-making of all stakeholders in the aviation industry and thus has been attempted by various previous research.
Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments
This paper explores the optimization of Ground Delay Programs (GDP), a prevalent Traffic Management Initiative used in Air Traffic Management (ATM) to reconcile capacity and demand discrepancies at airports.
SwapTalk: Audio-Driven Talking Face Generation with One-Shot Customization in Latent Space
To enhance the framework's generalization capabilities for unseen identities, we incorporate identity loss during the training of the face swapping module.
Brighteye: Glaucoma Screening with Color Fundus Photographs based on Vision Transformer
Optic disc detection improves the sensitivity at 95% specificity from 79. 20% to 85. 70% for glaucoma detection and the Hamming distance from 0. 2470 to 0. 1250 for glaucomatous feature classification.
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 #2 on
Automated Essay Scoring
on ASAP-AES
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 #14 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 #19 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)
+3
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.
