1 code implementation • NeurIPS 2021 • Zilin Gao, Qilong Wang, Bingbing Zhang, QinGhua Hu, Peihua Li
Then, a temporal covariance pooling performs temporal pooling of the attentive covariance representations to characterize both intra-frame correlations and inter-frame cross-correlations of the calibrated features.
1 code implementation • NeurIPS 2018 • Qilong Wang, Zilin Gao, Jiangtao Xie, WangMeng Zuo, Peihua Li
However, both GAP and existing HOP methods assume unimodal distributions, which cannot fully capture statistics of convolutional activations, limiting representation ability of deep CNNs, especially for samples with complex contents.
1 code implementation • CVPR 2019 • Zilin Gao, Jiangtao Xie, Qilong Wang, Peihua Li
Deep Convolutional Networks (ConvNets) are fundamental to, besides large-scale visual recognition, a lot of vision tasks.
4 code implementations • CVPR 2018 • Peihua Li, Jiangtao Xie, Qilong Wang, Zilin Gao
Towards addressing this problem, we propose an iterative matrix square root normalization method for fast end-to-end training of global covariance pooling networks.
Ranked #14 on Fine-Grained Image Classification on CUB-200-2011
Fine-Grained Image Classification Fine-Grained Image Recognition