D Bai, G Li, D Jiang, J Yun, B Tao, G Jiang… - … Applications of Artificial …, 2024 - Elsevier
Industrial products typically lack defects in smart manufacturing systems, which leads to an extremely imbalanced task of recognizing surface defects. With this imbalanced sample …
Deep long-tailed learning, one of the most challenging problems in visual recognition, aims to train well-performing deep models from a large number of images that follow a long-tailed …
Real-world data typically follow a long-tailed distribution, where a few majority categories occupy most of the data while most minority categories contain a limited number of samples …
Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision. Leveraging data augmentations is a …
J Cui, Z Zhong, S Liu, B Yu… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle long-tailed recognition. Based on theoretical analysis, we observe supervised contrastive loss tends to …
In the real open world, data tends to follow long-tailed class distributions, motivating the well- studied long-tailed recognition (LTR) problem. Naive training produces models that are …
We present a causal inference framework to improve Weakly-Supervised Semantic Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by …
S Zhang, Z Li, S Yan, X He… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Despite the success of the deep neural networks, it remains challenging to effectively build a system for long-tail visual recognition tasks. To address this problem, we first investigate the …
Real-world data often exhibit imbalanced distributions, where certain target values have significantly fewer observations. Existing techniques for dealing with imbalanced data focus …