36 citations · 75 across the 7 of their papers we have counts for
7 papers
Harnessing the Power of Local Representations for Few-Shot Classification
Shi Tang, Guiming Luo, Xinchen Ye +1
Generalizing to novel classes unseen during training is a key challenge of few-shot classification. Recent metric-based methods try to address this by local representations. Howeve…
RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos
Yunhui Han, Kunming Luo, Ao Luo +4
Obtaining the ground truth labels from a video is challenging since the manual annotation of pixel-wise flow labels is prohibitively expensive and laborious. Besides, existing appr…
Parallel ensemble methods for causal direction inference
Yulai Zhang, Jiachen Wang, Gang Cen +1
Inferring the causal direction between two variables from their observation data is one of the most fundamental and challenging topics in data science. A causal direction inference…
Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network
Chao Peng, Xiangyu Zhang, Gang Yu +2
One of recent trends [30, 31, 14] in network architec- ture design is stacking small filters (e.g., 1x1 or 3x3) in the entire network because the stacked small filters is more ef-…
Some Supplementaries to The Counting Semantics for Abstract Argumentation
Fuan Pu, Jian Luo, Guiming Luo
Dung's abstract argumentation framework consists of a set of interacting arguments and a series of semantics for evaluating them. Those semantics partition the powerset of the set…
Attacker and Defender Counting Approach for Abstract Argumentation
Fuan Pu, Jian Luo, Yulai Zhang +1
In Dung's abstract argumentation, arguments are either acceptable or unacceptable, given a chosen notion of acceptability. This gives a coarse way to compare arguments. In this pap…