Search Results for author:
Found 77 papers, 36 papers with code
Contrastive Learning for Implicit Social Factors in Social Media Popularity Prediction
Extensive experiments on the Social Media Popularity Dataset validate the superiority of our proposed method and also confirm the important role of implicit social factors in popularity prediction.
NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional Mamba
In addition, we design a traffic representation scheme to extract valid information from massive traffic data while removing biased information.
LidaRF: Delving into Lidar for Neural Radiance Field on Street Scenes
In this paper, we propose several insights that allow a better utilization of Lidar data to improve NeRF quality on street scenes.
BiLO: Bilevel Local Operator Learning for PDE inverse problems
At the upper level, we minimize the data loss with respect to the PDE parameters.
Large Language Models for Networking: Workflow, Advances and Challenges
The networking field is characterized by its high complexity and rapid iteration, requiring extensive expertise to accomplish network tasks, ranging from network design, configuration, diagnosis and security.
Improving Legal Case Retrieval with Brain Signals
Traditional feedback signal such as clicks is too coarse to use as they do not reflect any fine-grained relevance information.
Integrating Efficient Optimal Transport and Functional Maps For Unsupervised Shape Correspondence Learning
In the realm of computer vision and graphics, accurately establishing correspondences between geometric 3D shapes is pivotal for applications like object tracking, registration, texture transfer, and statistical shape analysis.
MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers
Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control.
Relevance Feedback with Brain Signals
To explore the effectiveness of brain signals in the context of RF, we propose a novel RF framework that combines BCI-based relevance feedback with pseudo-relevance signals and implicit signals to improve the performance of document re-ranking.
Adaptive Image Registration: A Hybrid Approach Integrating Deep Learning and Optimization Functions for Enhanced Precision
Image registration has traditionally been done using two distinct approaches: learning based methods, relying on robust deep neural networks, and optimization-based methods, applying complex mathematical transformations to warp images accordingly.
CVTHead: One-shot Controllable Head Avatar with Vertex-feature Transformer
Reconstructing personalized animatable head avatars has significant implications in the fields of AR/VR.
HierCas: Hierarchical Temporal Graph Attention Networks for Popularity Prediction in Information Cascades
In this paper, we propose Hierarchical Temporal Graph Attention Networks for cascade popularity prediction (HierCas), which operates on the entire cascade graph by a dynamic graph modeling approach.
GNN4EEG: A Benchmark and Toolkit for Electroencephalography Classification with Graph Neural Network
Recognizing this shortfall, there has been a burgeoning interest in recent years in harnessing the potential of Graph Neural Networks (GNN) to exploit the topological information by modeling features selected from each EEG channel in a graph structure.
Light Field Diffusion for Single-View Novel View Synthesis
Starting from the camera pose matrices, LFD transforms them into light field encoding, with the same shape as the reference image, to describe the direction of each ray.
Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learning
In conclusion, we present a novel approach for rapid online adaptation in interactive image segmentation, adapting to a new organ in just 0. 83 minutes.
On-the-Fly Guidance Training for Medical Image Registration
Weakly-supervised methods struggle due to the scarcity of labeled data, and unsupervised methods directly depend on image similarity metrics for accuracy.
Ranked #1 on
Medical Image Registration
on OASIS
View while Moving: Efficient Video Recognition in Long-untrimmed Videos
To this end, inspired by human cognition, we propose a novel recognition paradigm of "View while Moving" for efficient long-untrimmed video recognition.
Improving Social Media Popularity Prediction with Multiple Post Dependencies
Social Media Popularity Prediction has drawn a lot of attention because of its profound impact on many different applications, such as recommendation systems and multimedia advertising.
Hybrid-CSR: Coupling Explicit and Implicit Shape Representation for Cortical Surface Reconstruction
We present Hybrid-CSR, a geometric deep-learning model that combines explicit and implicit shape representations for cortical surface reconstruction.
Hybrid Neural Diffeomorphic Flow for Shape Representation and Generation via Triplane
Deep Implicit Functions (DIFs) have gained popularity in 3D computer vision due to their compactness and continuous representation capabilities.
Diffeomorphic Mesh Deformation via Efficient Optimal Transport for Cortical Surface Reconstruction
The metric is defined by sliced Wasserstein distance on meshes represented as probability measures that generalize the set-based approach.
MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation
Then, we use an image sequence generator and semantic diffusion refiner conditioned on the generated mask sequences to produce realistic 3D medical images that align with the generated masks.
T2Ranking: A large-scale Chinese Benchmark for Passage Ranking
T2Ranking comprises more than 300K queries and over 2M unique passages from real-world search engines.
Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation
In this paper, we propose a novel contrastive learning framework that integrates Localized Region Contrast (LRC) to enhance existing self-supervised pre-training methods for medical image segmentation.
Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training
Vision transformers (ViTs) have recently obtained success in many applications, but their intensive computation and heavy memory usage at both training and inference time limit their generalization.
Identity-Aware Hand Mesh Estimation and Personalization from RGB Images
In this paper, we propose an identity-aware hand mesh estimation model, which can incorporate the identity information represented by the intrinsic shape parameters of the subject.
PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation
In this paper, we propose the token-Pruned Pose Transformer (PPT) for 2D human pose estimation, which can locate a rough human mask and performs self-attention only within selected tokens.
Ranked #18 on
3D Human Pose Estimation
on Human3.6M
(using extra training data)
Brain Topography Adaptive Network for Satisfaction Modeling in Interactive Information Access System
We explore the effectiveness of BTA for satisfaction modeling in two popular information access scenarios, i. e., search and recommendation.
Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval
By making the REM and DAMs disentangled, DDR enables a flexible training paradigm in which REM is trained with supervision once and DAMs are trained with unsupervised data.
Medical Image Registration via Neural Fields
Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity between two images.
Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models
For example, representation-based retrieval models perform almost as well as interaction-based retrieval models in terms of interpolation but not extrapolation.
Topology-Preserving Shape Reconstruction and Registration via Neural Diffeomorphic Flow
Recently DIFs-based methods have been proposed to handle shape reconstruction and dense point correspondences simultaneously, capturing semantic relationships across shapes of the same class by learning a DIFs-modeled shape template.
Diffeomorphic Image Registration with Neural Velocity Field
Here we propose a new optimization-based method named DNVF (Diffeomorphic Image Registration with Neural Velocity Field) which utilizes deep neural network to model the space of admissible transformations.
EI-CLIP: Entity-Aware Interventional Contrastive Learning for E-Commerce Cross-Modal Retrieval
recommendation, and marketing services.
Pre-training Methods in Information Retrieval
The core of information retrieval (IR) is to identify relevant information from large-scale resources and return it as a ranked list to respond to the user's information need.
AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation
One has to either downsample the image or use cropped local patches to reduce GPU memory usage, which limits its performance.
TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation
The 3D position encoding guided by the epipolar field provides an efficient way of encoding correspondences between pixels of different views.
Ranked #22 on
3D Human Pose Estimation
on Human3.6M
(using extra training data)
Web Search via an Efficient and Effective Brain-Machine Interface
While search technologies have evolved to be robust and ubiquitous, the fundamental interaction paradigm has remained relatively stable for decades.
Stingy Teacher: Sparse Logits Suffice to Fail Knowledge Distillation
However, it is unclear why the distorted distribution of the logits is catastrophic to the student model.
Why Don't You Click: Neural Correlates of Non-Click Behaviors in Web Search
Inspired by these findings, we conduct supervised learning tasks to estimate the usefulness of non-click results with brain signals and conventional information (i. e., content and context factors).
Towards a Better Understanding Human Reading Comprehension with Brain Signals
In this paper, we carefully design a lab-based user study to investigate brain activities during reading comprehension.
Recurrent Mask Refinement for Few-Shot Medical Image Segmentation
Although having achieved great success in medical image segmentation, deep convolutional neural networks usually require a large dataset with manual annotations for training and are difficult to generalize to unseen classes.
Undistillable: Making A Nasty Teacher That CANNOT teach students
Knowledge Distillation (KD) is a widely used technique to transfer knowledge from pre-trained teacher models to (usually more lightweight) student models.
Test-Time Training for Deformable Multi-Scale Image Registration
Registration is a fundamental task in medical robotics and is often a crucial step for many downstream tasks such as motion analysis, intra-operative tracking and image segmentation.
Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation
State-of-the-art CNN segmentation models apply either 2D or 3D convolutions on input images, with pros and cons associated with each method: 2D convolution is fast, less memory-intensive but inadequate for extracting 3D contextual information from volumetric images, while the opposite is true for 3D convolution.
DGGAN: Depth-image Guided Generative Adversarial Networks for Disentangling RGB and Depth Images in 3D Hand Pose Estimation
Estimating3D hand poses from RGB images is essentialto a wide range of potential applications, but is challengingowing to substantial ambiguity in the inference of depth in-formation from RGB images.
MVHM: A Large-Scale Multi-View Hand Mesh Benchmark for Accurate 3D Hand Pose Estimation
Based on the match algorithm, we propose an efficient pipeline to generate a large-scale multi-view hand mesh (MVHM) dataset with accurate 3D hand mesh and joint labels.
Temporal-Aware Self-Supervised Learning for 3D Hand Pose and Mesh Estimation in Videos
Experiments show that our modelachieves surprisingly good results, with 3D estimation ac-curacy on par with the state-of-the-art models trained with3D annotations, highlighting the benefit of the temporalconsistency in constraining 3D prediction models.
SIA-GCN: A Spatial Information Aware Graph Neural Network with 2D Convolutions for Hand Pose Estimation
In this paper, we extend GNNs along two directions: a) allowing features at each node to be represented by 2D spatial confidence maps instead of 1D vectors; and b) proposing an efficient operation to integrate information from neighboring nodes through 2D convolutions with different learnable kernels at each edge.
Rotation-invariant Mixed Graphical Model Network for 2D Hand Pose Estimation
In this paper, we propose a new architecture named Rotation-invariant Mixed Graphical Model Network (R-MGMN) to solve the problem of 2D hand pose estimation from a monocular RGB image.
Nonparametric Structure Regularization Machine for 2D Hand Pose Estimation
We propose a novel Nonparametric Structure Regularization Machine (NSRM) for 2D hand pose estimation, adopting a cascade multi-task architecture to learn hand structure and keypoint representations jointly.
AttentionAnatomy: A unified framework for whole-body organs at risk segmentation using multiple partially annotated datasets
Organs-at-risk (OAR) delineation in computed tomography (CT) is an important step in Radiation Therapy (RT) planning.
A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction
The ABCD Neurocognitive Prediction Challenge is a community driven competition asking competitors to develop algorithms to predict fluid intelligence score from T1-w MRIs.
Dynamically Pruned Message Passing Networks for Large-Scale Knowledge Graph Reasoning
We propose Dynamically Pruned Message Passing Networks (DPMPN) for large-scale knowledge graph reasoning.
Adaptive Graphical Model Network for 2D Handpose Estimation
In this paper, we propose a new architecture called Adaptive Graphical Model Network (AGMN) to tackle the task of 2D hand pose estimation from a monocular RGB image.
NoduleNet: Decoupled False Positive Reductionfor Pulmonary Nodule Detection and Segmentation
Pulmonary nodule detection, false positive reduction and segmentation represent three of the most common tasks in the computeraided analysis of chest CT images.
Neural Multi-Scale Self-Supervised Registration for Echocardiogram Dense Tracking
In this work, we propose a neural multi-scale self-supervised registration (NMSR) method for automated myocardial and cardiac blood flow dense tracking.
An End-to-end Framework For Integrated Pulmonary Nodule Detection and False Positive Reduction
Most of the existing deep learning nodule detection systems are constructed in two steps: a) nodule candidates screening and b) false positive reduction, using two different models trained separately.
Automatic Pulmonary Lobe Segmentation Using Deep Learning
To validate the robustness and performance of our proposed framework trained with a small number of training examples, we further tested our model on CT scans from an independent dataset.
Automated pulmonary nodule detection using 3D deep convolutional neural networks
Finally, we introduce a method to ensemble models from both stages via consensus to give the final predictions.
Parallel Clustering of Single Cell Transcriptomic Data with Split-Merge Sampling on Dirichlet Process Mixtures
Motivation: With the development of droplet based systems, massive single cell transcriptome data has become available, which enables analysis of cellular and molecular processes at single cell resolution and is instrumental to understanding many biological processes.
Sequential Embedding Induced Text Clustering, a Non-parametric Bayesian Approach
In this paper, the documents are modeled as the joint of bags of words, sequential features and word embeddings.
Generating Realistic Training Images Based on Tonality-Alignment Generative Adversarial Networks for Hand Pose Estimation
Hand pose estimation from a monocular RGB image is an important but challenging task.
AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy
Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot.
Content-based Video Relevance Prediction Challenge: Data, Protocol, and Baseline
Video relevance prediction is one of the most important tasks for online streaming service.
DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection
Recently deep learning has been witnessing widespread adoption in various medical image applications.
Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification
In this work, we present a deep learning framework for multi-class breast cancer image classification as our submission to the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology images (BACH).
Breast Cancer Histology Image Classification
General Classification
+2
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
DeepLung consists of two components, nodule detection (identifying the locations of candidate nodules) and classification (classifying candidate nodules into benign or malignant).
Ranked #5 on
Lung Nodule Classification
on LIDC-IDRI
Structured Triplet Learning with POS-tag Guided Attention for Visual Question Answering
Visual question answering (VQA) is of significant interest due to its potential to be a strong test of image understanding systems and to probe the connection between language and vision.
Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
Mass segmentation provides effective morphological features which are important for mass diagnosis.
DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification
Considering the 3D nature of lung CT data, two 3D networks are designed for the nodule detection and classification respectively.
Automated Pulmonary Nodule Detection And Classification
Classification
+2
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs.
HLA class I binding prediction via convolutional neural networks
We then propose a deep convolutional neural network architecture, name HLA-CNN, for the task of HLA class I-peptide binding prediction.
Adversarial Deep Structural Networks for Mammographic Mass Segmentation
Experimental results on two public datasets, INbreast and DDSM-BCRP, show that our end-to-end network combined with adversarial training achieves the-state-of-the-art results.
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data.
Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks
Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions.
Inference in Kingman's Coalescent with Particle Markov Chain Monte Carlo Method
We propose a new algorithm to do posterior sampling of Kingman's coalescent, based upon the Particle Markov Chain Monte Carlo methodology.
