123 citations · 244 across the 36 of their papers we have counts for
36 papers
Mask6D: Masked Pose Priors For 6D Object Pose Estimation
Yuechen Xie, Haobo Jiang, Jin Xie
Robust 6D object pose estimation in cluttered or occluded conditions using monocular RGB images remains a challenging task. One reason is that current pose estimation networks stru…
Attr-Int: A Simple and Effective Entity Alignment Framework for Heterogeneous Knowledge Graphs
Linyan Yang, Jingwei Cheng, Chuanhao Xu +3
Entity alignment (EA) refers to the task of linking entities in different knowledge graphs (KGs). Existing EA methods rely heavily on structural isomorphism. However, in real-world…
Towards General Deepfake Detection with Dynamic Curriculum
Wentang Song, Yuzhen Lin, Bin Li
Most previous deepfake detection methods bent their efforts to discriminate artifacts by end-to-end training. However, the learned networks often fail to mine the general face forg…
Strategic Arms with Side Communication Prevail Over Low-Regret MAB Algorithms
Ahmed Ben Yahmed, Clément Calauzènes, Vianney Perchet
In the strategic multi-armed bandit setting, when arms possess perfect information about the player's behavior, they can establish an equilibrium where: 1. they retain almost all o…
Sec2Sec Co-attention for Video-Based Apparent Affective Prediction
Mingwei Sun, Kunpeng Zhang
Video-based apparent affect detection plays a crucial role in video understanding, as it encompasses various elements such as vision, audio, audio-visual interactions, and spatiote…
T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Image
Shijie Zhang, Boyan Jiang, Keke He +5
Pixel2Mesh (P2M) is a classical approach for reconstructing 3D shapes from a single color image through coarse-to-fine mesh deformation. Although P2M is capable of generating plaus…
Maximum-Entropy Adversarial Audio Augmentation for Keyword Spotting
Zuzhao Ye, Gregory Ciccarelli, Brian Kulis
Data augmentation is a key tool for improving the performance of deep networks, particularly when there is limited labeled data. In some fields, such as computer vision, augmentati…
DarkShot: Lighting Dark Images with Low-Compute and High-Quality
Jiazhang Zheng, Lei Li, Qiuping Liao +3
Nighttime photography encounters escalating challenges in extremely low-light conditions, primarily attributable to the ultra-low signal-to-noise ratio. For real-world deployment,…
TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks
Khondoker Murad Hossain, Tim Oates
As deep neural networks and the datasets used to train them get larger, the default approach to integrating them into research and commercial projects is to download a pre-trained…
TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processing
Luyuan Xie, Cong Li, Xin Zhang +4
Representation learning frameworks in unlabeled time series have been proposed for medical signal processing. Despite the numerous excellent progresses have been made in previous w…
GMTR: Graph Matching Transformers
Jinpei Guo, Shaofeng Zhang, Runzhong Wang +2
Vision transformers (ViTs) have recently been used for visual matching beyond object detection and segmentation. However, the original grid dividing strategy of ViTs neglects the s…
Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identification
Mohammed Maqsood Shaik, Dietrich Klakow, Badr M. Abdullah
Pre-trained Transformer-based speech models have shown striking performance when fine-tuned on various downstream tasks such as automatic speech recognition and spoken language ide…
Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adapter
Song Li, Yongbin You, Xuezhi Wang +2
Multilingual intelligent assistants, such as ChatGPT, have recently gained popularity. To further expand the applications of multilingual artificial intelligence assistants and fac…
How Can Context Help? Exploring Joint Retrieval of Passage and Personalized Context
Hui Wan, Hongkang Li, Songtao Lu +2
The integration of external personalized context information into document-grounded conversational systems has significant potential business value, but has not been well-studied.…
Federated Neuro-Symbolic Learning
Pengwei Xing, Songtao Lu, Han Yu
Neuro-symbolic learning (NSL) models complex symbolic rule patterns into latent variable distributions by neural networks, which reduces rule search space and generates unseen rule…
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities
Zhuqing Liu, Xin Zhang, Songtao Lu +1
Recently, min-max optimization problems have received increasing attention due to their wide range of applications in machine learning (ML). However, most existing min-max solution…
Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data
Yonggui Yan, Jie Chen, Pin-Yu Chen +3
We first propose a decentralized proximal stochastic gradient tracking method (DProxSGT) for nonconvex stochastic composite problems, with data heterogeneously distributed on multi…
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
Shuai Zhang, Meng Wang, Pin-Yu Chen +3
Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and sto…
Stochastic Inexact Augmented Lagrangian Method for Nonconvex Expectation Constrained Optimization
Zichong Li, Pin-Yu Chen, Sijia Liu +2
Many real-world problems not only have complicated nonconvex functional constraints but also use a large number of data points. This motivates the design of efficient stochastic me…
ASGNN: Graph Neural Networks with Adaptive Structure
Zepeng Zhang, Songtao Lu, Zengfeng Huang +1
The graph neural network (GNN) models have presented impressive achievements in numerous machine learning tasks. However, many existing GNN models are shown to be vulnerable to adv…
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks
Zhuqing Liu, Xin Zhang, Prashant Khanduri +2
In recent years, decentralized bilevel optimization problems have received increasing attention in the networking and machine learning communities thanks to their versatility in mo…
Understanding Benign Overfitting in Gradient-Based Meta Learning
Lisha Chen, Songtao Lu, Tianyi Chen
Meta learning has demonstrated tremendous success in few-shot learning with limited supervised data. In those settings, the meta model is usually overparameterized. While the conve…
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
Gaoyuan Zhang, Songtao Lu, Yihua Zhang +7
Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against…
Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning
Alex Gu, Songtao Lu, Parikshit Ram +1
We consider a generic min-max multi-objective bilevel optimization problem with applications in robust machine learning such as representation learning and hyperparameter optimizat…