Search Results for author:
Found 5 papers, 1 papers with code
Attention Discriminant Sampling for Point Clouds
The proposed attention discriminant sampling (ADS) starts by efficiently decomposing a given point cloud into clusters to implicitly encode its structural and geometric relatedness among points.
Decoupled Contrastive Learning
Further, DCL can be combined with the SOTA contrastive learning method, NNCLR, to achieve 72. 3% ImageNet-1K top-1 accuracy with 512 batch size in 400 epochs, which represents a new SOTA in contrastive learning.
Natural World Distribution via Adaptive Confusion Energy Regularization
The Fine-Grained Visual Classification (FGVC) problem is notably characterized by two intriguing properties, significant inter-class similarity and intra-class variations, which cause learning an effective FGVC classifier a challenging task.
Fine-Grained Visual Classification with Batch Confusion Norm
We introduce a regularization concept based on the proposed Batch Confusion Norm (BCN) to address Fine-Grained Visual Classification (FGVC).
Ranked #17 on
Fine-Grained Image Classification
on FGVC Aircraft
ACE: Adaptive Confusion Energy for Natural World Data Distribution
With the development of deep learning, standard classification problems have achieved good results.
Fine-Grained Image Classification
Fine-Grained Visual Recognition
