no code implementations • 30 Apr 2021 • Lu Yang, Yunlong Wang, Lingqiao Liu, Peng Wang, Lu Chi, Zehuan Yuan, Changhu Wang, Yanning Zhang
In this paper, we propose a new loss based on center predictivity, that is, a sample must be positioned in a location of the feature space such that from it we can roughly predict the location of the center of same-class samples.
1 code implementation • NeurIPS 2020 • Lu Chi, Borui Jiang, Yadong Mu
FFC is a generic operator that can directly replace vanilla convolutions in a large body of existing networks, without any adjustments and with comparable complexity metrics (e. g., FLOPs).
3 code implementations • CVPR 2020 • Lu Chi, Zehuan Yuan, Yadong Mu, Changhu Wang
The core of our method is learnable and data-adaptive bilinear attentional transform (BA-Transform), whose merits are three-folds: first, BA-Transform is versatile to model a wide spectrum of local or global attentional operations, such as emphasizing specific local regions.
1 code implementation • MM '19: Proceedings of the 27th ACM International Conference on Multimedia 2019 • Lu Chi, Guiyu Tian, Yadong Mu, Lingxi Xie, Qi Tian
We show its equivalence to conducting residual learning in some spectral domain and carefully re-formulate a variety of neural layers into their spectral forms, such as ReLU or convolutions.
no code implementations • 1 Aug 2019 • Lu Chi, Guiyu Tian, Yadong Mu, Qi Tian
In the experiments, we comprehensively compare our method with two-stream and non-local models widely used in video classification.
Ranked #32 on Action Recognition on UCF101
1 code implementation • 12 Aug 2017 • Lu Chi, Yadong Mu
There are multiple fronts to these endeavors, including object detection on roads, 3-D reconstruction etc., but in this work we focus on a vision-based model that directly maps raw input images to steering angles using deep networks.