12 citations · 36 across the 10 of their papers we have counts for
14 papers
CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks
Haotian Xue, Kaixiong Zhou, Tianlong Chen +4
Despite the recent advances of graph neural networks (GNNs) in modeling graph data, the training of GNNs on large datasets is notoriously hard due to the overfitting. Adversarial t…
Wanderlust: Online Continual Object Detection in the Real World
Jianren Wang, Xin Wang, Yue Shang-Guan +1
Online continual learning from data streams in dynamic environments is a critical direction in the computer vision field. However, realistic benchmarks and fundamental studies in t…
Dynamic Multi-scale Convolution for Dialect Identification
Tianlong Kong, Shouyi Yin, Dawei Zhang +6
Time Delay Neural Networks (TDNN)-based methods are widely used in dialect identification. However, in previous work with TDNN application, subtle variant is being neglected in dif…
TML-AP: Adversarial Attacks to Top- Multi-Label Learning
Shu Hu, Lipeng Ke, Xin Wang +1
Top- multi-label learning, which returns the top- predicted labels from an input, has many practical applications such as image annotation, document analysis, and web search…
Robust Object Detection via Instance-Level Temporal Cycle Confusion
Xin Wang, Thomas E. Huang, Benlin Liu +4
Building reliable object detectors that are robust to domain shifts, such as various changes in context, viewpoint, and object appearances, is critical for real-world applications.…
Learning by Minimizing the Sum of Ranked Range
Shu Hu, Yiming Ying, Xin Wang +1
In forming learning objectives, one oftentimes needs to aggregate a set of individual values to a single output. Such cases occur in the aggregate loss, which combines individual l…