Search Results for author:
Found 38 papers, 7 papers with code
UnClE: Explicitly Leveraging Semantic Similarity to Reduce the Parameters of Word Embeddings
Inspired by the fact that words with similar semantic can share a part of weights, we divide the embeddings of words into two parts: unique embedding and class embedding.
One-Class Knowledge Distillation for Face Presentation Attack Detection
Under this framework, a teacher network is trained with source domain samples to provide discriminative feature representations for face PAD.
Estimating the Resize Parameter in End-to-end Learned Image Compression
By conducting extensive experimental tests on existing deep image compression models, we show results that our new resizing parameter estimation framework can provide Bj{\o}ntegaard-Delta rate (BD-rate) improvement of about 10% against leading perceptual quality engines.
Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction
To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i. e., preference topics generation, preference shift modeling, and social influence activation.
A Preliminary Research on Space Situational Awareness Based on Event Cameras
Event camera is a new type of sensor that is different from traditional cameras.
Improving Maximum Likelihood Difference Scaling method to measure inter content scale
The goal of most subjective studies is to place a set of stimuli on a perceptual scale.
ABG: A Multi-Party Mixed Protocol Framework for Privacy-Preserving Cooperative Learning
To address the issue of privacy-preserving in collaborative learning, secure outsourced computation and federated learning are two typical methods.
Asymmetric Modality Translation For Face Presentation Attack Detection
In this paper, we propose a novel framework based on asymmetric modality translation for face presentation attack detection in bi-modality scenarios.
Learning Meta Pattern for Face Anti-Spoofing
To improve the generalization ability, recent hybrid methods have been explored to extract task-aware handcrafted features (e. g., Local Binary Pattern) as discriminative information for the input of DNNs.
AutoChart: A Dataset for Chart-to-Text Generation Task
The analytical description of charts is an exciting and important research area with many applications in academia and industry.
KuiLeiXi: a Chinese Open-Ended Text Adventure Game
Equipped with GPT-2 and the latest GPT-3, AI Dungeon has been seen as a famous example of the powerful text generation capabilities of large-scale pre-trained language models, and a possibility for future games.
Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective
Usually, this prediction is always with great challenges to making a comprehensive understanding of both the start-up project and market environment.
Convolutional Block Design for Learned Fractional Downsampling
The layers of convolutional neural networks (CNNs) can be used to alter the resolution of their inputs, but the scaling factors are limited to integer values.
BSTC: A Large-Scale Chinese-English Speech Translation Dataset
This corpus is expected to promote the research of automatic simultaneous translation as well as the development of practical systems.
Color image segmentation based on a convex K-means approach
The proposed variational method uses a combination of $l_1$ and $l_2$ regularizers to maintain edge information of objects in images while overcoming the staircase effect.
Enhancing VMAF through New Feature Integration and Model Combination
VMAF is a machine learning based video quality assessment method, originally designed for streaming applications, which combines multiple quality metrics and video features through SVM regression.
Learning Skill Equivalencies Across Platform Taxonomies
Assessment and reporting of skills is a central feature of many digital learning platforms.
Learning the Implicit Semantic Representation on Graph-Structured Data
Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole, while the implicit semantic associations behind highly complex interactions of graphs are largely unexploited.
Origin of the Electronic Structure in Single-Layer FeSe/SrTiO3 Films
These observations not only show the first direct evidence that the electronic structure of single-layer FeSe/SrTiO3 films originates from bulk FeSe through a combined effect of an electronic phase transition and an interfacial charge transfer, but also provide a quantitative basis for theoretical models in describing the electronic structure and understanding the superconducting mechanism in single-layer FeSe/SrTiO3 films.
Band Gap
Superconductivity
Strongly Correlated Electrons
Observation of Magnetic Droplets in Magnetic Tunnel Junctions
Magnetic droplets, a class of highly non-linear magnetodynamical solitons, can be nucleated and stabilized in nanocontact spin-torque nano-oscillators where they greatly increase the microwave output power.
Applied Physics
Dynamic radiomics: a new methodology to extract quantitative time-related features from tomographic images
The feature extraction methods of radiomics are mainly based on static tomographic images at a certain moment, while the occurrence and development of disease is a dynamic process that cannot be fully reflected by only static characteristics.
Strategy for Boosting Pair Comparison and Improving Quality Assessment Accuracy
The development of rigorous quality assessment model relies on the collection of reliable subjective data, where the perceived quality of visual multimedia is rated by the human observers.
Sequence-to-Sequence Load Disaggregation Using Multi-Scale Residual Neural Network
Secondly, we propose dilated convolution to curtail the excessive quantity of model parameters and obtain bigger receptive field, and multi-scale structure to learn mixed data features in a more targeted way.
MMEA: Entity Alignment for Multi-Modal Knowledge Graphs
To that end, in this paper, we propose a novel solution called Multi-Modal Entity Alignment (MMEA) to address the problem of entity alignment in a multi-modal view.
Perceptually Optimizing Deep Image Compression
Mean squared error (MSE) and $\ell_p$ norms have largely dominated the measurement of loss in neural networks due to their simplicity and analytical properties.
Learning the Compositional Visual Coherence for Complementary Recommendations
Towards this end, in this paper, we propose a novel Content Attentive Neural Network (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents.
Can Machines “Learn” Halide Perovskite Crystal Formation without Accurate Physicochemical Features?
To address limitations in previous work, we developed an improved description of the reactant concentrations in the experiments (validated against experimental observations) and performed experiments quantifying the excess volume of mixing of γ-butyrolactone/formic acid mixtures used in the perovskite syntheses.
GPM: A Generic Probabilistic Model to Recover Annotator's Behavior and Ground Truth Labeling
In the big data era, data labeling can be obtained through crowdsourcing.
Estimating Early Fundraising Performance of Innovations via Graph-based Market Environment Model
In the crowdfunding market, the early fundraising performance of the project is a concerned issue for both creators and platforms.
ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compression
By building on top of an existing deep image compression model, we are able to demonstrate a bitrate reduction of as much as $31\%$ over MSE optimization, given a specified perceptual quality (VMAF) level.
On Boosting Single-Frame 3D Human Pose Estimation via Monocular Videos
As illustrated in experiments, given only a small set of annotations, our method successfully makes the model to learn new poses from unlabelled monocular videos, promoting the accuracies of the baseline model by about 10%.
Ranked #10 on
Weakly-supervised 3D Human Pose Estimation
on Human3.6M
Generalized Score Distribution
A class of discrete probability distributions contains distributions with limited support, i. e. possible argument values are limited to a set of numbers (typically consecutive).
Methodology Multimedia G.3
Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach
When making cloth decisions, people usually show preferences for different semantic attributes (e. g., the clothes with v-neck collar).
Ranked #2 on
Recommendation Systems
on Amazon Fashion
(using extra training data)
Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors
In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users' historical stable preferences and present consumption motivations.
ServeNet: A Deep Neural Network for Web Services Classification
Automated service classification plays a crucial role in service discovery, selection, and composition.
A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates
This paper proposes a novel proximal-gradient algorithm for a decentralized optimization problem with a composite objective containing smooth and non-smooth terms.
Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification
In this paper, we propose a multi-scale deep feature learning method for high-resolution satellite image classification.
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
An adversarial example library for constructing attacks, building defenses, and benchmarking both
