Counterfactual zero-shot and open-set visual recognition
We present a novel counterfactual framework for both Zero-Shot Learning (ZSL) and Open-Set Recognition (OSR), whose common challenge is generalizing to the unseen-classes by only training on the seen-classes. Our idea stems from the observation that the generated samples for unseen-classes are often...
Saved in:
Main Authors: | YUE, Zhongqi, WANG, Tan, SUN, Qianru, HUA, Xian-Sheng, ZHANG, Hanwang |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2021
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/6120 https://ink.library.smu.edu.sg/context/sis_research/article/7123/viewcontent/counterfactual_zsl_openset_CVPR2021.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Low-shot Object Detection via Classification Refinement.
by: Li, Yiting, et al.
Published: (2020) -
Where is my spot? Few-shot image generation via latent subspace optimization
by: ZHENG, Chenxi, et al.
Published: (2023) -
Voice pattern recognition system
by: Azahari, Azhar, et al.
Published: (1991) -
Learning adversarial semantic embeddings for zero-shot recognition in open worlds
by: LI, Tianqi, et al.
Published: (2024) -
Hyperbolic Visual Embedding Learning for Zero-Shot Recognition
by: Shaoteng Liu, et al.
Published: (2020)