Search Results for author:
Found 23 papers, 6 papers with code
ROG$_{PL}$: Robust Open-Set Graph Learning via Region-Based Prototype Learning
In specific, ROG$_{PL}$ consists of two modules, i. e., denoising via label propagation and open-set prototype learning via regions.
ParsNets: A Parsimonious Orthogonal and Low-Rank Linear Networks for Zero-Shot Learning
Then, to facilitate the generalization of local linearities, we construct a maximal margin geometry on the learned features by enforcing low-rank constraints on intra-class samples and high-rank constraints on inter-class samples, resulting in orthogonal subspaces for different classes and each subspace lies on a compact manifold.
Pretrain like Your Inference: Masked Tuning Improves Zero-Shot Composed Image Retrieval
Then, we propose a masked tuning, which uses the text and the masked image to learn the modifications of the original image.
Ranking-aware Uncertainty for Text-guided Image Retrieval
Specifically, our approach mainly comprises three components: (1) In-sample uncertainty, which aims to capture semantic diversity using a Gaussian distribution derived from both combined and target features; (2) Cross-sample uncertainty, which further mines the ranking information from other samples' distributions; and (3) Distribution regularization, which aligns the distributional representations of source inputs and targeted image.
Ranked #3 on
Image Retrieval
on Fashion IQ
Improving Rating Prediction in Multi-Criteria Recommender Systems via a Collective Factor Model
Experiments on three real-world datasets show that our proposed architecture achieves up to 13. 14% lower prediction error over baseline approaches.
MvCo-DoT:Multi-View Contrastive Domain Transfer Network for Medical Report Generation
In clinical scenarios, multiple medical images with different views are usually generated at the same time, and they have high semantic consistency.
Open-World Pose Transfer via Sequential Test-Time Adaption
In open-world conditions, the pose transfer task raises various independent signals: OOD appearance and skeleton, which need to be extracted and distributed in speciality.
MPS-AMS: Masked Patches Selection and Adaptive Masking Strategy Based Self-Supervised Medical Image Segmentation
We leverage the masked patches selection strategy to choose masked patches with lesions to obtain more lesion representation information, and the adaptive masking strategy is utilized to help learn more mutual information and improve performance further.
Multi-Head Feature Pyramid Networks for Breast Mass Detection
Analysis of X-ray images is one of the main tools to diagnose breast cancer.
OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup
Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities.
MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation
In this paper, we introduce a novel Multi-View Approach with Hybrid Attentive Networks (MV-HAN) for contents retrieval at the matching stage of recommender systems.
Hybrid Reinforced Medical Report Generation with M-Linear Attention and Repetition Penalty
In this article, we propose a hybrid reinforced medical report generation method with m-linear attention and repetition penalty mechanism (HReMRG-MR) to overcome these problems.
Field-aware Variational Autoencoders for Billion-scale User Representation Learning
Moreover, we deploy the proposed method in real-world applications and conduct online A/B tests in a look-alike system.
DnSwin: Toward Real-World Denoising via Continuous Wavelet Sliding-Transformer
Real-world image denoising is a practical image restoration problem that aims to obtain clean images from in-the-wild noisy inputs.
Predicting ratings in multi-criteria recommender systems via a collective factor model
In a multi-criteria recommender system, users are allowed to give an overall rating to an item and provide a score on each of its attribute.
Ranked #1 on
Recommendation Systems
on BeerAdvocate
A Unified Joint Maximum Mean Discrepancy for Domain Adaptation
From the revealed unified JMMD, we illustrate that JMMD degrades the feature-label dependence (discriminability) that benefits to classification, and it is sensitive to the label distribution shift when the label kernel is the weighted class conditional one.
SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images
We first generate MRI images on limited datasets, then we trained three popular classification models to get the best model for tumor classification.
Efficient Medical Image Segmentation with Intermediate Supervision Mechanism
However, U-Net is mainly engaged in segmentation, and the extracted features are also targeted at segmentation location information, and the input and output are different.
w-Net: Dual Supervised Medical Image Segmentation Model with Multi-Dimensional Attention and Cascade Multi-Scale Convolution
Non-local attention and feature learning by multi-scale methods are widely used to model network, which drives progress in medical image segmentation.
Joint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network
In the field of multimedia, single image deraining is a basic pre-processing work, which can greatly improve the visual effect of subsequent high-level tasks in rainy conditions.
DCSFN: Deep Cross-scale Fusion Network for Single Image Rain Removal
Further, we design an inner-scale connection block to utilize the multi-scale information and features fusion way between different scales to improve rain representation ability and we introduce the dense block with skip connection to inner-connect these blocks.
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
D2D-Enabled Data Sharing for Distributed Machine Learning at Wireless Network Edge
Mobile edge learning is an emerging technique that enables distributed edge devices to collaborate in training shared machine learning models by exploiting their local data samples and communication and computation resources.
