40 citations · 88 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…