Search Results for author:
Found 30 papers, 11 papers with code
Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning
Document-level relation extraction aims to recognize relations among multiple entity pairs from a whole piece of article.
ERCache: An Efficient and Reliable Caching Framework for Large-Scale User Representations in Meta's Ads System
The increasing complexity of deep learning models used for calculating user representations presents significant challenges, particularly with limited computational resources and strict service-level agreements (SLAs).
PTQ4RIS: Post-Training Quantization for Referring Image Segmentation
Specifically, we first conduct an in-depth analysis of the root causes of performance degradation in RIS model quantization and propose dual-region quantization (DRQ) and reorder-based outlier-retained quantization (RORQ) to address the quantization difficulties in visual and text encoders.
Advanced Unstructured Data Processing for ESG Reports: A Methodology for Structured Transformation and Enhanced Analysis
In the evolving field of corporate sustainability, analyzing unstructured Environmental, Social, and Governance (ESG) reports is a complex challenge due to their varied formats and intricate content.
Empowering Working Memory for Large Language Model Agents
The limitations of traditional LLM memory designs are analyzed, including their isolation of distinct dialog episodes and lack of persistent memory links.
DKiS: Decay weight invertible image steganography with private key
Image steganography, defined as the practice of concealing information within another image, traditionally encounters security challenges when its methods become publicly known or are under attack.
PRIS: Practical robust invertible network for image steganography
Most of the existing image steganography methods have low hiding robustness when the container images affected by distortion.
Rapid-INR: Storage Efficient CPU-free DNN Training Using Implicit Neural Representation
This paper introduces Rapid-INR, a novel approach that utilizes INR for encoding and compressing images, thereby accelerating neural network training in computer vision tasks.
Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation
Using machine learning to solve combinatorial optimization (CO) problems is challenging, especially when the data is unlabeled.
High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing
The contribution of this work is three-fold.
Fast and accurate waveform modeling of long-haul multi-channel optical fiber transmission using a hybrid model-data driven scheme
The modeling of optical wave propagation in optical fiber is a task of fast and accurate solving the nonlinear Schr\"odinger equation (NLSE), and can enable the optical system design, digital signal processing verification and fast waveform calculation.
TE-YOLOF: Tiny and efficient YOLOF for blood cell detection
In this work, an object detector based on YOLOF has been proposed to detect blood cell objects such as red blood cells, white blood cells and platelets.
Document-level Event Extraction via Parallel Prediction Networks
We argue that sentence-level extractors are ill-suited to the DEE task where event arguments always scatter across sentences and multiple events may co-exist in a document.
A mechanistic-based data-driven approach to accelerate structural topology optimization through finite element convolutional neural network (FE-CNN)
During offline training, a mapping function is built between high and low resolution representations of a given design domain.
DPN-SENet:A self-attention mechanism neural network for detection and diagnosis of COVID-19 from chest x-ray images
We propose a deep learning model that can help radiologists and clinicians use chest X-rays to diagnose COVID-19 cases and show the diagnostic features of pneumonia.
Pyramid Point Cloud Transformer for Large-Scale Place Recognition
In order to obtain discriminative global descriptors, we construct a pyramid VLAD module to aggregate the multi-scale feature maps of point clouds into the global descriptors.
Ranked #3 on
3D Place Recognition
on Oxford RobotCar Dataset
Peer-to-Peer Localization for Single-Antenna Devices
In this paper, we present P2PLocate, a peer-to-peer localization system that enables a single-antenna device co-located with a batteryless backscatter tag to localize another single-antenna device with decimeter-level accuracy.
Reconstructing Event Regions for Event Extraction via Graph Attention Networks
Event information is usually scattered across multiple sentences within a document.
Channel-wise Alignment for Adaptive Object Detection
Existing methods on this task usually draw attention on the high-level alignment based on the whole image or object of interest, which naturally, cannot fully utilize the fine-grained channel information.
Select Good Regions for Deblurring based on Convolutional Neural Networks
The goal of blind image deblurring is to recover sharp image from one input blurred image with an unknown blur kernel.
Fast and Accurate Optical Fiber Channel Modeling Using Generative Adversarial Network
In this work, a new data-driven fiber channel modeling method, generative adversarial network (GAN) is investigated to learn the distribution of fiber channel transfer function.
Population pharmacokinetics and dosing regimen optimization of tacrolimus in Chinese lung transplant recipients
Given the nonlinear kinetics of tacrolimus and large variability, population pharmacokinetic model should be combined with therapeutic drug monitoring to optimize individualized therapy.
Depth Image Upsampling based on Guided Filter with Low Gradient Minimization
In our framework, the upscaling of a low-resolution depth image is guided by a corresponding intensity images, we formulate it as a cost aggregation problem with the guided filter.
A Robust Local Binary Similarity Pattern for Foreground Object Detection
First, we propose a robust texture operator named Robust Local Binary Similarity Pattern (RLBSP), which shows strong robustness to illumination variations and dynamic backgrounds.
Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms
Traditional approaches to the task of ACE event detection primarily regard multiple events in one sentence as independent ones and recognize them separately by using sentence-level information.
DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data
We present an event extraction framework to detect event mentions and extract events from the document-level financial news.
Guided Filter based Edge-preserving Image Non-blind Deconvolution
In the denoising step, the guided filter is used with the two obtained images for efficient edge-preserving filtering.
An Adaptive Parameter Estimation for Guided Filter based Image Deconvolution
Image deconvolution is still to be a challenging ill-posed problem for recovering a clear image from a given blurry image, when the point spread function is known.
Structured Indoor Modeling
The grammar then drives a principled new reconstruction algorithm, where the grammar rules are sequentially applied to recover a structured model.
Dual Domain Filters Based Texture and Structure Preserving Image Non-Blind Deconvolution
Image deconvolution continues to be an active research topic of recovering a sharp image, given a blurry one generated by a convolution.
