35 citations · 81 across the 9 of their papers we have counts for
17 papers · 1 filter
InstructMixup: Instruction-Guided Salient Patch Editing for Robust Data Augmentation
Khawar Islam, Arif Mahmood, Xin Jin +1
In image and video technologies, data augmentation is widely used to improve the generalization of deep visual models, and mixup-based strategies that interpolate between samples h…
-FracMix: Label-Preserving Self-Saliency Mixup Augmentation
Khawar Islam, Arif Mahmood, Xin Jin +1
Data augmentation is known to improve generalization of deep visual models. Recent methods favor mixup strategies that generate interpolated samples to improve model performance. H…
Structure-preserving Feature Alignment for Old Photo Colorization
Yingxue Pang, Xin Jin, Jun Fu +1
Deep learning techniques have made significant advancements in reference-based colorization by training on large-scale datasets. However, directly applying these methods to the tas…
Re-energizing Domain Discriminator with Sample Relabeling for Adversarial Domain Adaptation
Xin Jin, Cuiling Lan, Wenjun Zeng +1
Many unsupervised domain adaptation (UDA) methods exploit domain adversarial training to align the features to reduce domain gap, where a feature extractor is trained to fool a dom…
Local Patch AutoAugment with Multi-Agent Collaboration
Shiqi Lin, Tao Yu, Ruoyu Feng +3
Data augmentation (DA) plays a critical role in improving the generalization of deep learning models. Recent works on automatically searching for DA policies from data have achieve…
Dense Interaction Learning for Video-based Person Re-identification
Tianyu He, Xin Jin, Xu Shen +3
Video-based person re-identification (re-ID) aims at matching the same person across video clips. Efficiently exploiting multi-scale fine-grained features while building the struct…