Search Results for author:
Found 10 papers, 9 papers with code
DropCov: A Simple yet Effective Method for Improving Deep Architectures
Particularly, we for the first time show that \textit{effective post-normalization can make a good trade-off between representation decorrelation and information preservation for GCP, which are crucial to alleviate over-fitting and increase representation ability of deep GCP networks, respectively}.
Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification
Few-shot classification is a challenging problem as only very few training examples are given for each new task.
Binocular Mutual Learning for Improving Few-shot Classification
From the perspective of class space on base set, existing methods either focus on utilizing all classes under a global view by normal pretraining, or pay more attention to adopt an episodic manner to train meta-tasks within few classes in a local view.
3rd Place Solution for Short-video Face Parsing Challenge
Benefiting from the edge information and edge attention loss, the proposed EANet achieves 86. 16\% accuracy in the Short-video Face Parsing track of the 3rd Person in Context (PIC) Workshop and Challenge, ranked the third place.
So-ViT: Mind Visual Tokens for Vision Transformer
Therefore, we propose a new classification paradigm, where the second-order, cross-covariance pooling of visual tokens is combined with class token for final classification.
Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
The proposed methods are highly modular, readily plugged into existing deep CNNs.
Ranked #1 on
Image Classification
on iNaturalist
(Top 3 Error metric)
Global Gated Mixture of Second-order Pooling for Improving Deep Convolutional Neural Networks
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.
Global Second-order Pooling Convolutional Networks
Deep Convolutional Networks (ConvNets) are fundamental to, besides large-scale visual recognition, a lot of vision tasks.
Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization
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
Is Second-order Information Helpful for Large-scale Visual Recognition?
The main challenges involved are robust covariance estimation given a small sample of large-dimensional features and usage of the manifold structure of covariance matrices.
