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07/13/2023
FF-LINS: A Consistent Frame-to-Frame Solid-State-LiDAR-Inertial State Estimator
Most of the existing LiDAR-inertial navigation systems are based on fram...
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05/22/2023
PO-VINS: An Efficient Pose-Only LiDAR-Enhanced Visual-Inertial State Estimator
The pose-only (PO) visual representation has been proven to be equivalen...
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12/22/2022
On Calibrating Semantic Segmentation Models: Analysis and An Algorithm
We study the problem of semantic segmentation calibration. For image cla...
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12/03/2021
CTIN: Robust Contextual Transformer Network for Inertial Navigation
Recently, data-driven inertial navigation approaches have demonstrated t...
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09/18/2021
Anti-Neuron Watermarking: Protecting Personal Data Against Unauthorized Neural Model Training
In this paper, we raise up an emerging personal data protection problem ...
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07/24/2021
A Survey of Semantics-Aware Performance Optimization for Data-Intensive Computing
We are living in the era of Big Data and witnessing the explosion of dat...
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07/24/2021
SODA: A Semantics-Aware Optimization Framework for Data-Intensive Applications Using Hybrid Program Analysis
In the era of data explosion, a growing number of data-intensive computi...
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01/20/2021
Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19
An accurate and efficient forecasting system is imperative to the preven...
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11/23/2020
Ranking Neural Checkpoints
This paper is concerned with ranking many pre-trained deep neural networ...
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11/13/2020
Cross-Domain Learning for Classifying Propaganda in Online Contents
As news and social media exhibit an increasing amount of manipulative po...
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10/23/2020
Beyond the Deep Metric Learning: Enhance the Cross-Modal Matching with Adversarial Discriminative Domain Regularization
Matching information across image and text modalities is a fundamental c...
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07/17/2020
Improving Object Detection with Selective Self-supervised Self-training
We study how to leverage Web images to augment human-curated object dete...
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07/02/2020
Trace-Norm Adversarial Examples
White box adversarial perturbations are sought via iterative optimizatio...
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03/31/2020
Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox Model
We study how to train a student deep neural network for visual recogniti...
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03/26/2020
BachGAN: High-Resolution Image Synthesis from Salient Object Layout
We propose a new task towards more practical application for image gener...
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03/24/2020
Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective
Object frequency in the real world often follows a power law, leading to...
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02/13/2020
Self-supervised learning for audio-visual speaker diarization
Speaker diarization, which is to find the speech segments of specific sp...
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11/21/2019
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning
There is an increasing number of pre-trained deep neural network models....
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06/16/2019
Defending Against Adversarial Attacks Using Random Forests
As deep neural networks (DNNs) have become increasingly important and po...
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05/30/2019
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness
Deep Neural Network (DNN) trained by the gradient descent method is know...
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05/08/2019
Frame-Recurrent Video Inpainting by Robust Optical Flow Inference
In this paper, we present a new inpainting framework for recovering miss...
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05/01/2019
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
Powerful adversarial attack methods are vital for understanding how to c...
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02/15/2019
Learning to Adaptively Scale Recurrent Neural Networks
Recent advancements in recurrent neural network (RNN) research have demo...
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02/05/2019
Asynchronous Delay-Aware Accelerated Proximal Coordinate Descent for Nonconvex Nonsmooth Problems
Nonconvex and nonsmooth problems have recently attracted considerable at...
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02/03/2019
Depthwise Convolution is All You Need for Learning Multiple Visual Domains
There is a growing interest in designing models that can deal with image...
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01/14/2019
AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data
The success of deep neural networks often relies on a large amount of la...
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10/17/2018
A Proximal Zeroth-Order Algorithm for Nonconvex Nonsmooth Problems
In this paper, we focus on solving an important class of nonconvex optim...
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07/11/2018
How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization
The large volume of video content and high viewing frequency demand auto...
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03/05/2018
Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect
Despite being impactful on a variety of problems and applications, the g...
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02/08/2018