320 citations · 848 across the 30 of their papers we have counts for
20 papers · 1 filter
Recognizing Instagram Filtered Images with Feature De-stylization
Zhe Wu, Zuxuan Wu, Bharat Singh +1
Deep neural networks have been shown to suffer from poor generalization when small perturbations are added (like Gaussian noise), yet little work has been done to evaluate their ro…
LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition
Zuxuan Wu, Caiming Xiong, Yu-Gang Jiang +1
This paper presents LiteEval, a simple yet effective coarse-to-fine framework for resource efficient video recognition, suitable for both online and offline scenarios. Exploiting d…
Learning from Noisy Anchors for One-stage Object Detection
Hengduo Li, Zuxuan Wu, Chen Zhu +3
State-of-the-art object detectors rely on regressing and classifying an extensive list of possible anchors, which are divided into positive and negative samples based on their inte…
Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors
Zuxuan Wu, Ser-Nam Lim, Larry Davis +1
We present a systematic study of adversarial attacks on state-of-the-art object detection frameworks. Using standard detection datasets, we train patterns that suppress the objectn…
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost
Mingfei Gao, Zizhao Zhang, Guo Yu +3
Active learning (AL) combines data labeling and model training to minimize the labeling cost by prioritizing the selection of high value data that can best improve model performanc…
Cross-X Learning for Fine-Grained Visual Categorization
Wei Luo, Xitong Yang, Xianjie Mo +5
Recognizing objects from subcategories with very subtle differences remains a challenging task due to the large intra-class and small inter-class variation. Recent work tackles thi…