Search Results for author:
Found 43 papers, 17 papers with code
Global-guided Focal Neural Radiance Field for Large-scale Scene Rendering
The global stage obtains a continuous representation of the entire scene while the focal stage decomposes the scene into multiple blocks and further processes them with distinct sub-encoders.
MyPortrait: Morphable Prior-Guided Personalized Portrait Generation
We incorporate personalized prior in a monocular video and morphable prior in 3D face morphable space for generating personalized details under novel controllable parameters.
Cooperative Dual Attention for Audio-Visual Speech Enhancement with Facial Cues
By integrating cooperative dual attention in the visual encoder and audio-visual fusion strategy, our model effectively extracts beneficial speech information from both audio and visual cues for AVSE.
Learning Separable Hidden Unit Contributions for Speaker-Adaptive Lip-Reading
For deep layers where both the speaker's features and the speech content features are all expressed well, we introduce the speaker-adaptive features to learn for suppressing the speech content irrelevant noise for robust lip reading.
Unpaired Multi-domain Attribute Translation of 3D Facial Shapes with a Square and Symmetric Geometric Map
We propose a learning framework for 3D facial attribute translation to relieve these limitations.
Breaking the Curse of Quality Saturation with User-Centric Ranking
A key puzzle in search, ads, and recommendation is that the ranking model can only utilize a small portion of the vastly available user interaction data.
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation
Finding optimal configurations in a geometric space is a key challenge in many technological disciplines.
Model-based Constrained MDP for Budget Allocation in Sequential Incentive Marketing
Furthermore, we show that the dual problem can be solved by policy learning, with the optimal dual variable being found efficiently via bisection search (i. e., by taking advantage of the monotonicity).
UniCon+: ICTCAS-UCAS Submission to the AVA-ActiveSpeaker Task at ActivityNet Challenge 2022
This report presents a brief description of our winning solution to the AVA Active Speaker Detection (ASD) task at ActivityNet Challenge 2022.
UniCon: Unified Context Network for Robust Active Speaker Detection
Our solution is a novel, unified framework that focuses on jointly modeling multiple types of contextual information: spatial context to indicate the position and scale of each candidate's face, relational context to capture the visual relationships among the candidates and contrast audio-visual affinities with each other, and temporal context to aggregate long-term information and smooth out local uncertainties.
Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation
In this paper, we show that such efficiency highly depends on the scale at which the attack is applied, and attacking at the optimal scale significantly improves the efficiency.
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs
Combinatorial Optimization (CO) has been a long-standing challenging research topic featured by its NP-hard nature.
ICTCAS-UCAS-TAL Submission to the AVA-ActiveSpeaker Task at ActivityNet Challenge 2021
This report presents a brief description of our method for the AVA Active Speaker Detection (ASD) task at ActivityNet Challenge 2021.
Learning to Schedule DAG Tasks
Scheduling computational tasks represented by directed acyclic graphs (DAGs) is challenging because of its complexity.
Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks
We aim to bridge the gap between the two by investigating how to efficiently estimate gradient based on a projected low-dimensional space.
Learn an Effective Lip Reading Model without Pains
Considering the non-negligible effects of these strategies and the existing tough status to train an effective lip reading model, we perform a comprehensive quantitative study and comparative analysis, for the first time, to show the effects of several different choices for lip reading.
Ranked #1 on
Lipreading
on CAS-VSR-W1k (LRW-1000)
(using extra training data)
Learning (Re-)Starting Solutions for Vehicle Routing Problems
A key challenge in solving a combinatorial optimization problem is how to guide the agent (i. e., solver) to efficiently explore the enormous search space.
Variational Optimization for the Submodular Maximum Coverage Problem
We examine the \emph{submodular maximum coverage problem} (SMCP), which is related to a wide range of applications.
Bandit Samplers for Training Graph Neural Networks
However, due to the intractable computation of optimal sampling distribution, these sampling algorithms are suboptimal for GCNs and are not applicable to more general graph neural networks (GNNs) where the message aggregator contains learned weights rather than fixed weights, such as Graph Attention Networks (GAT).
Ranked #1 on
Node Property Prediction
on ogbn-proteins
QEBA: Query-Efficient Boundary-Based Blackbox Attack
Such adversarial attacks can be achieved by adding a small magnitude of perturbation to the input to mislead model prediction.
You Only Search Once: A Fast Automation Framework for Single-Stage DNN/Accelerator Co-design
DNN/Accelerator co-design has shown great potential in improving QoR and performance.
Synchronous Bidirectional Learning for Multilingual Lip Reading
Based on this idea, we try to explore the synergized learning of multilingual lip reading in this paper, and further propose a synchronous bidirectional learning (SBL) framework for effective synergy of multilingual lip reading.
A Learning-based Iterative Method for Solving Vehicle Routing Problems
This paper is concerned with solving combinatorial optimization problems, in particular, the capacitated vehicle routing problems (CVRP).
Secret Sharing based Secure Regressions with Applications
Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns.
Mutual Information Maximization for Effective Lip Reading
By combining these two advantages together, the proposed method is expected to be both discriminative and robust for effective lip reading.
Ranked #8 on
Lipreading
on CAS-VSR-W1k (LRW-1000)
Deformation Flow Based Two-Stream Network for Lip Reading
Observing on the continuity in adjacent frames in the speaking process, and the consistency of the motion patterns among different speakers when they pronounce the same phoneme, we model the lip movements in the speaking process as a sequence of apparent deformations in the lip region.
Ranked #6 on
Lipreading
on CAS-VSR-W1k (LRW-1000)
Industrial Scale Privacy Preserving Deep Neural Network
Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction.
Generating Natural Language Adversarial Examples on a Large Scale with Generative Models
It can attack text classification models with a higher success rate than existing methods, and provide acceptable quality for humans in the meantime.
Pseudo-Convolutional Policy Gradient for Sequence-to-Sequence Lip-Reading
On the one hand, we introduce the evaluation metric (refers to the character error rate in this paper) as a form of reward to optimize the model together with the original discriminative target.
Ranked #9 on
Lipreading
on CAS-VSR-W1k (LRW-1000)
Can We Read Speech Beyond the Lips? Rethinking RoI Selection for Deep Visual Speech Recognition
Recent advances in deep learning have heightened interest among researchers in the field of visual speech recognition (VSR).
Ranked #2 on
Lipreading
on GRID corpus (mixed-speech)
A Semi-supervised Graph Attentive Network for Financial Fraud Detection
Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.
Uncovering Insurance Fraud Conspiracy with Network Learning
In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information.
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing
In this paper, we present a graph representation learning method atop of transaction networks for merchant incentive optimization in mobile payment marketing.
Privacy Preserving PCA for Multiparty Modeling
In this paper, we present a general multiparty modeling paradigm with Privacy Preserving Principal Component Analysis (PPPCA) for horizontally partitioned data.
Solving Billion-Scale Knapsack Problems
Knapsack problems (KPs) are common in industry, but solving KPs is known to be NP-hard and has been tractable only at a relatively small scale.
Characterizing Attacks on Deep Reinforcement Learning
To the best of our knowledge, we are the first to apply adversarial attacks on DRL systems to physical robots.
Signet Ring Cell Detection With a Semi-supervised Learning Framework
Our framework achieves accurate signet ring cell detection and can be readily applied in the clinical trails.
Accurate Nuclear Segmentation with Center Vector Encoding
Nuclear segmentation is important and frequently demanded for pathology image analysis, yet is also challenging due to nuclear crowdedness and possible occlusion.
Multi-Task Learning for Audio Visual Active Speaker Detection
This report describes the approach underlying our submission to the active speaker detection task (task B-2) of ActivityNet Challenge 2019.
Ranked #17 on
Audio-Visual Active Speaker Detection
on AVA-ActiveSpeaker
(using extra training data)
Enhanced Network Embedding with Text Information
TENE learns the representations of nodes under the guidance of both proximity matrix which captures the network structure and text cluster membership matrix derived from clustering for text information.
LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild
It has shown a large variation in this benchmark in several aspects, including the number of samples in each class, video resolution, lighting conditions, and speakers' attributes such as pose, age, gender, and make-up.
Ranked #1 on
Lipreading
on LRW-1000
Multi-Feature Max-Margin Hierarchical Bayesian Model for Action Recognition
In this paper, a multi-feature max-margin hierarchical Bayesian model (M3HBM) is proposed for action recognition.
Multi-task Sparse Learning with Beta Process Prior for Action Recognition
In this paper, we formulate human action recognition as a novel Multi-Task Sparse Learning(MTSL) framework which aims to construct a test sample with multiple features from as few bases as possible.
