1 code implementation • 29 Nov 2023 • Shuangrui Ding, Rui Qian, Haohang Xu, Dahua Lin, Hongkai Xiong
In this paper, we propose a simple yet effective approach for self-supervised video object segmentation (VOS).
1 code implementation • 10 Jun 2022 • Haohang Xu, Shuangrui Ding, Xiaopeng Zhang, Hongkai Xiong, Qi Tian
Specifically, MRA consistently enhances the performance on supervised, semi-supervised as well as few-shot classification.
1 code implementation • 25 Nov 2021 • Yunjie Tian, Lingxi Xie, Xiaopeng Zhang, Jiemin Fang, Haohang Xu, Wei Huang, Jianbin Jiao, Qi Tian, Qixiang Ye
In this paper, we propose a self-supervised visual representation learning approach which involves both generative and discriminative proxies, where we focus on the former part by requiring the target network to recover the original image based on the mid-level features.
Ranked #63 on Semantic Segmentation on Cityscapes test
1 code implementation • CVPR 2022 • Shuangrui Ding, Maomao Li, Tianyu Yang, Rui Qian, Haohang Xu, Qingyi Chen, Jue Wang, Hongkai Xiong
To alleviate such bias, we propose \textbf{F}oreground-b\textbf{a}ckground \textbf{Me}rging (FAME) to deliberately compose the moving foreground region of the selected video onto the static background of others.
1 code implementation • ICLR 2022 • Haohang Xu, Jiemin Fang, Xiaopeng Zhang, Lingxi Xie, Xinggang Wang, Wenrui Dai, Hongkai Xiong, Qi Tian
Here bag of instances indicates a set of similar samples constructed by the teacher and are grouped within a bag, and the goal of distillation is to aggregate compact representations over the student with respect to instances in a bag.
no code implementations • 8 Jun 2021 • Bowen Shi, Xiaopeng Zhang, Haohang Xu, Wenrui Dai, Junni Zou, Hongkai Xiong, Qi Tian
This is achieved by first pretraining the network via the proposed pixel-to-prototype contrastive loss over multiple datasets regardless of their taxonomy labels, and followed by fine-tuning the pretrained model over specific dataset as usual.
no code implementations • 16 May 2021 • Yuhang Zhang, Xiaopeng Zhang, Robert. C. Qiu, Jie Li, Haohang Xu, Qi Tian
Semi-supervised learning acts as an effective way to leverage massive unlabeled data.
no code implementations • 4 Dec 2020 • Haohang Xu, Xiaopeng Zhang, Hao Li, Lingxi Xie, Hongkai Xiong, Qi Tian
In this paper, we propose a hierarchical semantic alignment strategy via expanding the views generated by a single image to \textbf{Cross-samples and Multi-level} representation, and models the invariance to semantically similar images in a hierarchical way.
no code implementations • 27 Jul 2020 • Haohang Xu, Hongkai Xiong, Guo-Jun Qi
In this paper, we propose the $K$-Shot Contrastive Learning (KSCL) of visual features by applying multiple augmentations to investigate the sample variations within individual instances.
no code implementations • 29 Dec 2019 • Haohang Xu, Hongkai Xiong, Guo-Jun Qi
To this end, we present a novel regularization mechanism by learning the change of feature representations induced by a distribution of transformations without using the labels of data examples.
no code implementations • 16 Nov 2019 • Feng Lin, Haohang Xu, Houqiang Li, Hongkai Xiong, Guo-Jun Qi
For this reason, we should use the geodesic to characterize how an image transform along the manifold of a transformation group, and adopt its length to measure the deviation between transformations.