17 citations · 58 across the 16 of their papers we have counts for
11 papers · 1 filter
Exploring Data Augmentations on Self-/Semi-/Fully- Supervised Pre-trained Models
Shentong Mo, Zhun Sun, Chao Li
Data augmentation has become a standard component of vision pre-trained models to capture the invariance between augmented views. In practice, augmentation techniques that mask reg…
What Can Simple Arithmetic Operations Do for Temporal Modeling?
Wenhao Wu, Yuxin Song, Zhun Sun +3
Temporal modeling plays a crucial role in understanding video content. To tackle this problem, previous studies built complicated temporal relations through time sequence thanks to…
Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation
Shentong Mo, Zhun Sun, Chao Li
Contrastive self-supervised learning (CSL) with a prototypical regularization has been introduced in learning meaningful representations for downstream tasks that require strong se…
Design of the topology for contrastive visual-textual alignment
Zhun Sun
Cosine similarity is the common choice for measuring the distance between the feature representations in contrastive visual-textual alignment learning. However, empirically a learn…
Siamese Prototypical Contrastive Learning
Shentong Mo, Zhun Sun, Chao Li
Contrastive Self-supervised Learning (CSL) is a practical solution that learns meaningful visual representations from massive data in an unsupervised approach. The ordinary CSL emb…
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
Wenhao Wu, Zhun Sun, Wanli Ouyang
Transferring knowledge from task-agnostic pre-trained deep models for downstream tasks is an important topic in computer vision research. Along with the growth of computational cap…
Improving Head Pose Estimation with a Combined Loss and Bounding Box Margin Adjustment
Mingzhen Shao, Zhun Sun, Mete Ozay +1
We address a problem of estimating pose of a person's head from its RGB image. The employment of CNNs for the problem has contributed to significant improvement in accuracy in rece…
Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration
Xing Liu, Masanori Suganuma, Zhun Sun +1
In this paper, we study design of deep neural networks for tasks of image restoration. We propose a novel style of residual connections dubbed "dual residual connection", which exp…
HyperNetworks with statistical filtering for defending adversarial examples
Zhun Sun, Mete Ozay, Takayuki Okatani
Deep learning algorithms have been known to be vulnerable to adversarial perturbations in various tasks such as image classification. This problem was addressed by employing severa…
Improving Robustness of Feature Representations to Image Deformations using Powered Convolution in CNNs
Zhun Sun, Mete Ozay, Takayuki Okatani
In this work, we address the problem of improvement of robustness of feature representations learned using convolutional neural networks (CNNs) to image deformation. We argue that…
Design of Kernels in Convolutional Neural Networks for Image Classification
Zhun Sun, Mete Ozay, Takayuki Okatani
Despite the effectiveness of Convolutional Neural Networks (CNNs) for image classification, our understanding of the relationship between shape of convolution kernels and learned r…