Search Results for author:
Found 17 papers, 3 papers with code
MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding
Large Vision-Language Models (LVLMs) have achieved remarkable performance in many vision-language tasks, yet their capabilities in fine-grained visual understanding remain insufficiently evaluated.
Neumann Series-based Neural Operator for Solving Inverse Medium Problem
The inverse medium problem, inherently ill-posed and nonlinear, presents significant computational challenges.
TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data
In this work, we address question answering (QA) over a hybrid of tabular and textual data that are very common content on the Web (e. g. SEC filings), where discrete reasoning capabilities are often required.
Graph Contrastive Learning with Generative Adversarial Network
Specifically, we present GACN, a novel Generative Adversarial Contrastive learning Network for graph representation learning.
PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation
Recommender systems play a crucial role in addressing the issue of information overload by delivering personalized recommendations to users.
Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search
In this paper, we design an efficient knowledge distillation framework for e-commerce relevance matching to integrate the respective advantages of Transformer-style models and classical relevance matching models.
TeKo: Text-Rich Graph Neural Networks with External Knowledge
Graph Neural Networks (GNNs) have gained great popularity in tackling various analytical tasks on graph-structured data (i. e., networks).
RDU: A Region-based Approach to Form-style Document Understanding
Key Information Extraction (KIE) is aimed at extracting structured information (e. g. key-value pairs) from form-style documents (e. g. invoices), which makes an important step towards intelligent document understanding.
DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks
The proposed method, named DSRGAN, includes a well designed detail extraction algorithm to capture the most important high frequency information from images.
FAMINet: Learning Real-time Semi-supervised Video Object Segmentation with Steepest Optimized Optical Flow
A FAMINet, which consists of a feature extraction network (F), an appearance network (A), a motion network (M), and an integration network (I), is proposed in this study to address the abovementioned problem.
Deep Joint Demosaicing and High Dynamic Range Imaging within a Single Shot
For the former challenge, a spatially varying convolution (SVC) is designed to process the Bayer images carried with varying exposures.
Rethinking Temperature in Graph Contrastive Learning
In this paper, we investigate and discuss what a good representation should be for a general loss (InfoNCE) in graph contrastive learning.
Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss
In this paper, a novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data.
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search
We propose in this paper a novel Second-order Relevance, which is fundamentally different from the previous First-order Relevance, to improve result relevance prediction.
BiTe-GCN: A New GCN Architecture via BidirectionalConvolution of Topology and Features on Text-Rich Networks
We propose BiTe-GCN, a novel GCN architecture with bidirectional convolution of both topology and features on text-rich networks to solve these limitations.
BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search
Relevance has significant impact on user experience and business profit for e-commerce search platform.
CORD-19: The COVID-19 Open Research Dataset
The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research.
