2 citations · 3 across the 3 of their papers we have counts for
4 papers
Attribute-formed Class-specific Concept Space: Endowing Language Bottleneck Model with Better Interpretability and Scalability
Jianyang Zhang, Qianli Luo, Guowu Yang +4
Language Bottleneck Models (LBMs) are proposed to achieve interpretable image recognition by classifying images based on textual concept bottlenecks. However, current LBMs simply l…
Adaptive Graph-based Generalized Regression Model for Unsupervised Feature Selection
Yanyong Huang, Zongxin Shen, Fuxu Cai +2
Unsupervised feature selection is an important method to reduce dimensions of high dimensional data without labels, which is benefit to avoid ``curse of dimensionality'' and improv…
Learning unbiased zero-shot semantic segmentation networks via transductive transfer
Haiyang Liu, Yichen Wang, Jiayi Zhao +2
Semantic segmentation, which aims to acquire a detailed understanding of images, is an essential issue in computer vision. However, in practical scenarios, new categories that are…
Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach
Qing Lian, Fengmao Lv, Lixin Duan +1
We propose a new approach, called self-motivated pyramid curriculum domain adaptation (PyCDA), to facilitate the adaptation of semantic segmentation neural networks from synthetic…