5 papers · 1 filter
Semantically Compatible Knowledge Distillation for Cross-Domain Object Detection with Vision Foundation Models
Qifeng Zhang, Ting Xiang, Zeyuan Bai +1
Vision foundation models (VFMs) offer strong generalization capabilities for domain-adaptive object detection (DAOD). However, existing VFM-based methods overlook the spatial-scale…
Learning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets
Ting Xiang, Chenxi Deng, Jinhui Zhao +4
Small-scale image classification is often limited by the scarcity of training data. Generative data augmentation (GDA) based on pretrained generative models has emerged as an effec…
MUSE: Resolving Manifold Misalignment in Visual Tokenization via Topological Orthogonality
Panqi Yang, Haodong Jing, Jiahao Chao +5
Unified visual tokenization faces a fundamental trade-off between high-fidelity pixel reconstruction (spatial equivariance) and semantic abstraction (conceptual invariance). We att…
Invisible Clean-Label Backdoor Attacks for Generative Data Augmentation
Ting Xiang, Jinhui Zhao, Changjian Chen +1
With the rapid advancement of image generative models, generative data augmentation has become an effective way to enrich training images, especially when only small-scale datasets…
Enhancing Small-Scale Dataset Expansion with Triplet-Connection-based Sample Re-Weighting
Ting Xiang, Changjian Chen, Zhuo Tang +5
The performance of computer vision models in certain real-world applications, such as medical diagnosis, is often limited by the scarcity of available images. Expanding datasets us…