40 citations · 88 across the 11 of their papers we have counts for
11 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…
A Pre-trained Data Deduplication Model based on Active Learning
Haochen Shi, Xinyao Liu, Fengmao Lv +4
In the era of big data, the issue of data quality has become increasingly prominent. One of the main challenges is the problem of duplicate data, which can arise from repeated entr…
CIMUFS: Complementary and Consensus Learning-based Incomplete Multi-view Unsupervised Feature Selection
Yanyong Huang, Zongxin Shen, Yuxin Cai +4
Multi-view unsupervised feature selection (MUFS) has been demonstrated as an effective technique to reduce the dimensionality of multi-view unlabeled data. The existing methods ass…
Self-Training Vision Language BERTs with a Unified Conditional Model
Xiaofeng Yang, Fengmao Lv, Fayao Liu +1
Natural language BERTs are trained with language corpus in a self-supervised manner. Unlike natural language BERTs, vision language BERTs need paired data to train, which restricts…
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…
Weakly-supervised Domain Adaption for Aspect Extraction via Multi-level Interaction Transfer
Tao Liang, Wenya Wang, Fengmao Lv
Fine-grained aspect extraction is an essential sub-task in aspect based opinion analysis. It aims to identify the aspect terms (a.k.a. opinion targets) of a product or service in e…
Incorporating Multiple Cluster Centers for Multi-Label Learning
Senlin Shu, Fengmao Lv, Yan Yan +3
Multi-label learning deals with the problem that each instance is associated with multiple labels simultaneously. Most of the existing approaches aim to improve the performance of…
Learning Cross-domain Semantic-Visual Relationships for Transductive Zero-Shot Learning
Fengmao Lv, Jianyang Zhang, Guowu Yang +3
Zero-Shot Learning (ZSL) learns models for recognizing new classes. One of the main challenges in ZSL is the domain discrepancy caused by the category inconsistency between trainin…
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…
MiniMax Entropy Network: Learning Category-Invariant Features for Domain Adaptation
Chaofan Tao, Fengmao Lv, Lixin Duan +1
How to effectively learn from unlabeled data from the target domain is crucial for domain adaptation, as it helps reduce the large performance gap due to domain shift or distributi…