18 citations · 46 across the 8 of their papers we have counts for
8 papers
Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation
Peng-Tao Jiang, Yuqi Yang
Weakly supervised semantic segmentation with weak labels is a long-lived ill-posed problem. Mainstream methods mainly focus on improving the quality of pseudo labels. In this repor…
Robust Meta Learning for Image based tasks
Penghao Jiang, Xin Ke, ZiFeng Wang +1
A machine learning model that generalizes well should obtain low errors on unseen test examples. Thus, if we learn an optimal model in training data, it could have better generaliz…
Invariant Meta Learning for Out-of-Distribution Generalization
Penghao Jiang, Ke Xin, Zifeng Wang +1
Modern deep learning techniques have illustrated their excellent capabilities in many areas, but relies on large training data. Optimization-based meta-learning train a model on a…
Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification
Peng Jiang, Juan Liu, Lang Wang +3
Labeling medical images depends on professional knowledge, making it difficult to acquire large amount of annotated medical images with high quality in a short time. Thus, making g…
Contrastive Learning of Features between Images and LiDAR
Peng Jiang, Srikanth Saripalli
Image and Point Clouds provide different information for robots. Finding the correspondences between data from different sensors is crucial for various tasks such as localization,…
A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels
Jillur Rahman Saurav, Mohammad Sadegh Nasr, Paul Koomey +7
Here we present a structural similarity index measure (SSIM) guided conditional Generative Adversarial Network (cGAN) that generatively performs image-to-image (i2i) synthesis to g…
Deep Learning based Intelligent Coin-tap Test for Defect Recognition
Hongyu Li, Peng Jiang, Tiejun Wang
The coin-tap test is a convenient and primary method for non-destructive testing, while its manual on-site operation is tough and costly. With the help of the latest intelligent si…
Self-Erasing Network for Integral Object Attention
Qibin Hou, Peng-Tao Jiang, Yunchao Wei +1
Recently, adversarial erasing for weakly-supervised object attention has been deeply studied due to its capability in localizing integral object regions. However, such a strategy r…