Search Results for author:
Found 17 papers, 7 papers with code
Speech2Lip: High-fidelity Speech to Lip Generation by Learning from a Short Video
Therefore, directly learning a mapping function from speech to the entire head image is prone to ambiguity, particularly when using a short video for training.
HRDoc: Dataset and Baseline Method Toward Hierarchical Reconstruction of Document Structures
Moreover, we proposed an encoder-decoder-based hierarchical document structure parsing system (DSPS) to tackle this problem.
Membership Inference Attacks Against Recommender Systems
In this paper, we make the first attempt on quantifying the privacy leakage of recommender systems through the lens of membership inference.
SEMv2: Table Separation Line Detection Based on Instance Segmentation
Table structure recognition is an indispensable element for enabling machines to comprehend tables.
Defensive Patches for Robust Recognition in the Physical World
For the generalization against diverse noises, we inject class-specific identifiable patterns into a confined local patch prior, so that defensive patches could preserve more recognizable features towards specific classes, leading models for better recognition under noises.
Multimodal Tree Decoder for Table of Contents Extraction in Document Images
Next, to parse the hierarchical relationship between the heading entities, a tree-structured decoder is designed.
Federated Learning Hyper-Parameter Tuning from a System Perspective
In this paper, we propose FedTune, an automatic FL hyper-parameter tuning algorithm tailored to applications' diverse system requirements in FL training.
Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detection
Collaborative learning allows multiple clients to train a joint model without sharing their data with each other.
Dual Synchronous Generator: Inertial Current Source based Grid-Forming Solution for VSC
Based on dual synchronous idea, a dual synchronous generator (DSG) control is applied in VSC to form inertial current source.
PM-MMUT: Boosted Phone-Mask Data Augmentation using Multi-Modeling Unit Training for Phonetic-Reduction-Robust E2E Speech Recognition
To boost the performance of PMT, we propose multi-modeling unit training (MMUT) architecture fusion with PMT (PM-MMUT).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
High Speed Rotation Estimation with Dynamic Vision Sensors
By designing a series of signal processing algorithms bespoke for dynamic vision sensing on mobile devices, EV-Tach is able to extract the rotational speed accurately from the event stream produced by dynamic vision sensing on rotary targets.
Learning Audio-Visual embedding for Person Verification in the Wild
It has already been observed that audio-visual embedding is more robust than uni-modality embedding for person verification.
Count, Decode and Fetch: A New Approach to Handwritten Chinese Character Error Correction
This poses a challenge when dealing with an unseen misspelled character, as the decoder may generate an IDS sequence that matches a seen character instead.
Hierarchical Audio-Visual Information Fusion with Multi-label Joint Decoding for MER 2023
Three different structures based on attention-guided feature gathering (AFG) are designed for deep feature fusion.
Incentivizing Massive Unknown Workers for Budget-Limited Crowdsensing: From Off-Line and On-Line Perspectives
How to incentivize strategic workers using limited budget is a very fundamental problem for crowdsensing systems; nevertheless, since the sensing abilities of the workers may not always be known as prior knowledge due to the diversities of their sensor devices and behaviors, it is difficult to properly select and pay the unknown workers.
Bidirectional Trained Tree-Structured Decoder for Handwritten Mathematical Expression Recognition
The Handwritten Mathematical Expression Recognition (HMER) task is a critical branch in the field of OCR.
Poisson-Gamma Dynamical Systems with Non-Stationary Transition Dynamics
Bayesian methodologies for handling count-valued time series have gained prominence due to their ability to infer interpretable latent structures and to estimate uncertainties, and thus are especially suitable for dealing with noisy and incomplete count data.
