6 citations · 16 across the 5 of their papers we have counts for
5 papers
Multi-dataset Pretraining: A Unified Model for Semantic Segmentation
Bowen Shi, Xiaopeng Zhang, Haohang Xu +4
Collecting annotated data for semantic segmentation is time-consuming and hard to scale up. In this paper, we for the first time propose a unified framework, termed as Multi-Datase…
Semi-supervised Contrastive Learning with Similarity Co-calibration
Yuhang Zhang, Xiaopeng Zhang, Robert. C. Qiu +3
Semi-supervised learning acts as an effective way to leverage massive unlabeled data. In this paper, we propose a novel training strategy, termed as Semi-supervised Contrastive Lea…
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning
Haohang Xu, Xiaopeng Zhang, Hao Li +3
Self-supervised learning based on instance discrimination has shown remarkable progress. In particular, contrastive learning, which regards each image as well as its augmentations…
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…