Search Results for author:
Found 26 papers, 10 papers with code
Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation
Hence, the key is to make full use of rich interaction information among streamers, users, and products.
Improving Person Re-identification with Iterative Impression Aggregation
Not only does such a simple method improve the performance of the baseline models, it also achieves comparable performance with latest advanced re-ranking methods.
Old Photo Restoration via Deep Latent Space Translation
Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize.
Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography
In this paper, we introduce a novel deep learning framework for mammogram image processing, which computes mass segmentation and simultaneously predict diagnosis results.
Bringing Old Photos Back to Life
Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize.
Density-Aware Graph for Deep Semi-Supervised Visual Recognition
Motivated by these limitations, this paper proposes to solve the SSL problem by building a novel density-aware graph, based on which the neighborhood information can be easily leveraged and the feature learning and label propagation can also be trained in an end-to-end way.
DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search
One common way is searching on a smaller proxy dataset (e. g., CIFAR-10) and then transferring to the target task (e. g., ImageNet).
Model Watermarking for Image Processing Networks
In this way, when the attacker trains one surrogate model by using the input-output pairs of the target model, the hidden watermark will be learned and extracted afterward.
Single image reflection removal via learning with multi-image constraints
Reflections are very common phenomena in our daily photography, which distract people's attention from the scene behind the glass.
Dynamic Convolution: Attention over Convolution Kernels
Light-weight convolutional neural networks (CNNs) suffer performance degradation as their low computational budgets constrain both the depth (number of convolution layers) and the width (number of channels) of CNNs, resulting in limited representation capability.
Ranked #905 on
Image Classification
on ImageNet
Once a MAN: Towards Multi-Target Attack via Learning Multi-Target Adversarial Network Once
Recently, generation-based methods have received much attention since they directly use feed-forward networks to generate the adversarial samples, which avoid the time-consuming iterative attacking procedure in optimization-based and gradient-based methods.
A General Decoupled Learning Framework for Parameterized Image Operators
To overcome this limitation, we propose a new decoupled learning algorithm to learn from the operator parameters to dynamically adjust the weights of a deep network for image operators, denoted as the base network.
Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis
Computer-aided breast cancer diagnosis in mammography is limited by inadequate data and the similarity between benign and cancerous masses.
Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation
Unsupervised domain adaptation aims to transfer the classifier learned from the source domain to the target domain in an unsupervised manner.
Inferring the Importance of Product Appearance: A Step Towards the Screenless Revolution
In this paper, we aim to infer the significance of every item's appearance in consumer decision making and identify the group of items that are suitable for screenless shopping.
Quantum-based subgraph convolutional neural networks
This new architecture captures both the global topological structure and the local connectivity structure within a graph.
Ranked #11 on
Graph Classification
on MUTAG
A Deep DUAL-PATH Network for Improved Mammogram Image Processing
We present, for the first time, a novel deep neural network architecture called \dcn with a dual-path connection between the input image and output class label for mammogram image processing.
Transductive Zero-Shot Learning with Visual Structure Constraint
Based on the observation that visual features of test instances can be separated into different clusters, we propose a new visual structure constraint on class centers for transductive ZSL, to improve the generality of the projection function (i. e. alleviate the above domain shift problem).
Emerging Applications of Reversible Data Hiding
Reversible data hiding (RDH) is one special type of information hiding, by which the host sequence as well as the embedded data can be both restored from the marked sequence without loss.
Geometry of Deep Learning for Magnetic Resonance Fingerprinting
Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy storage and computation requirements of a dictionary-matching (DM) step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications.
Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net
We explore the use of deep learning for breast mass segmentation in mammograms.
Decouple Learning for Parameterized Image Operators
Many different deep networks have been used to approximate, accelerate or improve traditional image operators, such as image smoothing, super-resolution and denoising.
Deep Exemplar-based Colorization
More importantly, as opposed to other learning-based colorization methods, our network allows the user to achieve customizable results by simply feeding different references.
Mirror, Mirror, on the Wall, Who's Got the Clearest Image of Them All? - A Tailored Approach to Single Image Reflection Removal
Removing reflection artefacts from a single image is a problem of both theoretical and practical interest, which still presents challenges because of the massively ill-posed nature of the problem.
Progressive Color Transfer with Dense Semantic Correspondences
The proposed method can be successfully extended from one-to-one to one-to-many color transfer.
