activity
20192022
most citedDisentangling Semantic-to-visual Confusion for Zero-shot Learning

30 citations · 44 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2022

SSD: Towards Better Text-Image Consistency Metric in Text-to-Image Generation

Zhaorui Tan, Xi Yang, Zihan Ye +4

Generating consistent and high-quality images from given texts is essential for visual-language understanding. Although impressive results have been achieved in generating high-qua…

cs.CV202130 cited

Disentangling Semantic-to-visual Confusion for Zero-shot Learning

Zihan Ye, Fuyuan Hu, Fan Lyu +2

Using generative models to synthesize visual features from semantic distribution is one of the most popular solutions to ZSL image classification in recent years. The triplet loss…

cs.LG20205 cited

Multi-Domain Multi-Task Rehearsal for Lifelong Learning

Fan Lyu, Shuai Wang, Wei Feng +3

Rehearsal, seeking to remind the model by storing old knowledge in lifelong learning, is one of the most effective ways to mitigate catastrophic forgetting, i.e., biased forgetting…

cs.CV20201 cited

Associating Multi-Scale Receptive Fields for Fine-grained Recognition

Zihan Ye, Fuyuan Hu, Yin Liu +3

Extracting and fusing part features have become the key of fined-grained image recognition. Recently, Non-local (NL) module has shown excellent improvement in image recognition. Ho…

cs.CV20198 cited

SR-GAN: Semantic Rectifying Generative Adversarial Network for Zero-shot Learning

Zihan Ye, Fan Lyu, Linyan Li +3

The existing Zero-Shot learning (ZSL) methods may suffer from the vague class attributes that are highly overlapped for different classes. Unlike these methods that ignore the disc…