420 citations · 1.6k across the 44 of their papers we have counts for
15 papers · 1 filter
FLAT: Few-Shot Learning via Autoencoding Transformation Regularizers
Haohang Xu, Hongkai Xiong, Guojun Qi
One of the most significant challenges facing a few-shot learning task is the generalizability of the (meta-)model from the base to the novel categories. Most of existing few-shot…
AETv2: AutoEncoding Transformations for Self-Supervised Representation Learning by Minimizing Geodesic Distances in Lie Groups
Feng Lin, Haohang Xu, Houqiang Li +2
Self-supervised learning by predicting transformations has demonstrated outstanding performances in both unsupervised and (semi-)supervised tasks. Among the state-of-the-art method…
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations
Xiao Wang, Daisuke Kihara, Jiebo Luo +1
Deep neural networks have been successfully applied to many real-world applications. However, such successes rely heavily on large amounts of labeled data that is expensive to obta…
GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-wise Transformations
Xiang Gao, Wei Hu, Guo-Jun Qi
Recent advances in Graph Convolutional Neural Networks (GCNNs) have shown their efficiency for non-Euclidean data on graphs, which often require a large amount of labeled data with…
Progressive Unsupervised Person Re-identification by Tracklet Association with Spatio-Temporal Regularization
Qiaokang Xie, Wengang Zhou, Guo-Jun Qi +2
Existing methods for person re-identification (Re-ID) are mostly based on supervised learning which requires numerous manually labeled samples across all camera views for training.…
An End-to-End Foreground-Aware Network for Person Re-Identification
Yiheng Liu, Wengang Zhou, Jianzhuang Liu +3
Person re-identification is a crucial task of identifying pedestrians of interest across multiple surveillance camera views. In person re-identification, a pedestrian is usually re…