101 citations · 146 across the 18 of their papers we have counts for
8 papers · 1 filter
Task-customized Masked AutoEncoder via Mixture of Cluster-conditional Experts
Zhili Liu, Kai Chen, Jianhua Han +4
Masked Autoencoder~(MAE) is a prevailing self-supervised learning method that achieves promising results in model pre-training. However, when the various downstream tasks have data…
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training
Xinchi Deng, Han Shi, Runhui Huang +7
Cross-modal pre-training has shown impressive performance on a wide range of downstream tasks, benefiting from massive image-text pairs collected from the Internet. In practice, on…
Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding
Jie Gui, Xiaofeng Cong, Lei He +2
On the one hand, the dehazing task is an illposedness problem, which means that no unique solution exists. On the other hand, the dehazing task should take into account the subject…
Fooling the Image Dehazing Models by First Order Gradient
Jie Gui, Xiaofeng Cong, Chengwei Peng +2
The research on the single image dehazing task has been widely explored. However, as far as we know, no comprehensive study has been conducted on the robustness of the well-trained…
Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning
Jidong Ge, Yuxiang Liu, Jie Gui +5
Self-supervised learning enables networks to learn discriminative features from massive data itself. Most state-of-the-art methods maximize the similarity between two augmentations…
AlignVE: Visual Entailment Recognition Based on Alignment Relations
Biwei Cao, Jiuxin Cao, Jie Gui +5
Visual entailment (VE) is to recognize whether the semantics of a hypothesis text can be inferred from the given premise image, which is one special task among recent emerged visio…