Search Results for author:
Found 12 papers, 4 papers with code
Practical Deep Dispersed Watermarking with Synchronization and Fusion
Compared with state-of-the-art approaches, our blind watermarking can achieve better performance: averagely improve the bit accuracy by 5. 28\% and 5. 93\% against single and combined attacks, respectively, and show less file size increment and better visual quality.
Towards the Universal Defense for Query-Based Audio Adversarial Attacks
In this work, we propose a novel method to infer the adversary intent and discover audio adversarial examples based on the AEs generation process.
Towards the Transferable Audio Adversarial Attack via Ensemble Methods
In this work, we explore the potential factors that impact adversarial examples (AEs) transferability in DL-based speech recognition.
Mechanics of Morphogenesis in Neural Development: in vivo, in vitro, and in silico
Due to the complexity and costs of in vivo and in vitro studies, a variety of computational models have been developed and used to explain the formation and morphogenesis of brain structures.
Smartphone-based Hard-braking Event Detection at Scale for Road Safety Services
Hard-braking events have been widely used as a safety surrogate due to their relatively high prevalence and ease of detection with embedded vehicle sensors.
Deep Dynamic Attention Model with Gate Mechanism for Solving Time-dependent Vehicle Routing Problems
We thus propose a Deep Dynamic Attention Models with Gate Mechanisms (DDAM-GM) to learn heuristics for time-dependent VRPs (TDVRPs) in real-world road networks.
Reconsidering Generative Objectives For Counterfactual Reasoning
As a step towards more flexible, scalable and accurate ITE estimation, we present a novel generative Bayesian estimation framework that integrates representation learning, adversarial matching and causal estimation.
PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line Features
This paper presents PL-VINS, a real-time optimization-based monocular VINS method with point and line features, developed based on the state-of-the-art point-based VINS-Mono \cite{vins}.
Salience and Market-aware Skill Extraction for Job Targeting
Based on the above promising results, we deployed the \model ~online to extract job targeting skills for all $20$M job postings served at LinkedIn.
The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation
Specifically, by investigating the standard data processing in state-of-the-art approaches mainly including coordinate system transformation and keypoint format transformation (i. e., encoding and decoding), we find that the results obtained by common flipping strategy are unaligned with the original ones in inference.
Ranked #14 on
Pose Estimation
on COCO test-dev
Semi-Supervised Adversarial Monocular Depth Estimation
In this paper, we address the problem of monocular depth estimation when only a limited number of training image-depth pairs are available.
AutoQ: Automated Kernel-Wise Neural Network Quantization
Recent network quantization techniques quantize each weight kernel in a convolutional layer independently for higher inference accuracy, since the weight kernels in a layer exhibit different variances and hence have different amounts of redundancy.
