30 citations · 68 across the 10 of their papers we have counts for
13 papers
Local Magnification for Data and Feature Augmentation
Kun He, Chang Liu, Stephen Lin +1
In recent years, many data augmentation techniques have been proposed to increase the diversity of input data and reduce the risk of overfitting on deep neural networks. In this wo…
Local Manifold Augmentation for Multiview Semantic Consistency
Yu Yang, Wing Yin Cheung, Chang Liu +1
Multiview self-supervised representation learning roots in exploring semantic consistency across data of complex intra-class variation. Such variation is not directly accessible an…
Beyond Instance Discrimination: Relation-aware Contrastive Self-supervised Learning
Yifei Zhang, Chang Liu, Yu Zhou +3
Contrastive self-supervised learning (CSL) based on instance discrimination typically attracts positive samples while repelling negatives to learn representations with pre-defined…
Learnable Expansion-and-Compression Network for Few-shot Class-Incremental Learning
Boyu Yang, Mingbao Lin, Binghao Liu +4
Few-shot class-incremental learning (FSCIL), which targets at continuously expanding model's representation capacity under few supervisions, is an important yet challenging problem…
Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection
Bohao Li, Boyu Yang, Chang Liu +3
Few-shot object detection has made substantial progressby representing novel class objects using the feature representation learned upon a set of base class objects. However,an imp…
Towards Spatio-Temporal Video Scene Text Detection via Temporal Clustering
Yuanqiang Cai, Chang Liu, Weiqiang Wang +1
With only bounding-box annotations in the spatial domain, existing video scene text detection (VSTD) benchmarks lack temporal relation of text instances among video frames, which h…