Search Results for author:
Found 18 papers, 9 papers with code
Segmentation-based Information Extraction and Amalgamation in Fundus Images for Glaucoma Detection
Glaucoma is a severe blinding disease, for which automatic detection methods are urgently needed to alleviate the scarcity of ophthalmologists.
Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction
Interpretable neural network models are of significant interest since they enhance the trustworthiness required in clinical practice when dealing with medical images.
TANet: Transformer-based Asymmetric Network for RGB-D Salient Object Detection
We employ the powerful feature extraction capability of Transformer (PVTv2) to extract global semantic information from RGB data and design a lightweight CNN backbone (LWDepthNet) to extract spatial structure information from depth data without pre-training.
Inverse design of nano-photonic wavelength demultiplexer with a deep neural network approach
The correlation coefficient of the prediction by the presented PTCN model remains greater than 0. 974 even when the size of training data is decreased to 17%.
Memory-Augmented Deep Conditional Unfolding Network for Pan-Sharpening
Pan-sharpening aims to obtain high-resolution multispectral (MS) images for remote sensing systems and deep learning-based methods have achieved remarkable success.
Highly accelerated MR parametric mapping by undersampling the k-space and reducing the contrast number simultaneously with deep learning
Methods: The proposed framework consists of a reconstruction module and a generative module.
Unfolding Taylor's Approximations for Image Restoration
Deep learning provides a new avenue for image restoration, which demands a delicate balance between fine-grained details and high-level contextualized information during recovering the latent clear image.
Unsupervised Domain Expansion for Visual Categorization
In this paper we extend UDA by proposing a new task called unsupervised domain expansion (UDE), which aims to adapt a deep model for the target domain with its unlabeled data, meanwhile maintaining the model's performance on the source domain.
Ranked #1 on
Unsupervised Domain Expansion
on UDE-DomainNet
SEA: Sentence Encoder Assembly for Video Retrieval by Textual Queries
Inspired by the initial success of previously few works in combining multiple sentence encoders, this paper takes a step forward by developing a new and general method for effectively exploiting diverse sentence encoders.
Ranked #2 on
Ad-hoc video search
on TRECVID-AVS16 (IACC.3)
(using extra training data)
Dual Encoding for Video Retrieval by Text
In this paper we achieve this by proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own.
Ranked #3 on
Ad-hoc video search
on TRECVID-AVS16 (IACC.3)
(using extra training data)
Feature Re-Learning with Data Augmentation for Video Relevance Prediction
Predicting the relevance between two given videos with respect to their visual content is a key component for content-based video recommendation and retrieval.
Automatically Generating Macro Research Reports from a Piece of News
As we all know, it requires the macro analysts to write such reports within a short period of time after the important economic news are released.
Hierarchical Attention Networks for Medical Image Segmentation
Unlike previous self-attention based methods that capture context information from one level, we reformulate the self-attention mechanism from the view of the high-order graph and propose a novel method, namely Hierarchical Attention Network (HANet), to address the problem of medical image segmentation.
Imagination Based Sample Construction for Zero-Shot Learning
Different from these existing types of methods, we propose a new method: sample construction to deal with the problem of ZSL.
Dual Encoding for Zero-Example Video Retrieval
This paper attacks the challenging problem of zero-example video retrieval.
Detect Globally, Refine Locally: A Novel Approach to Saliency Detection
Moreover, to effectively recover object boundaries, we propose a local Boundary Refinement Network (BRN) to adaptively learn the local contextual information for each spatial position.
Ranked #12 on
RGB Salient Object Detection
on DUTS-TE
COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrieval
This paper contributes to cross-lingual image annotation and retrieval in terms of data and baseline methods.
Adaptive Tag Selection for Image Annotation
The key insight is to divide the vocabulary into two disjoint subsets, namely a seen set consisting of tags having ground truth available for optimizing their thresholds and a novel set consisting of tags without any ground truth.
