Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation-Reference-Cited by-同舟云学术

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Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wu Likang¹^ORCID,Li Zhi²^ORCID,Zhao Hongke³^ORCID,Wang Zhefeng⁴^ORCID,Liu Qi¹^ORCID,Huai Baoxing⁴^ORCID,Yuan Nicholas Jing⁴^ORCID,Chen Enhong¹^ORCID

Affiliation:

1. University of Science and Technology of China, State Key Laboratory of Cognitive Intelligence, Hefei, China

2. Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

3. Tianjin University, Hefei, China

4. Huawei Cloud, Hangzhou, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

China Postdoctoral Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599486

Reference52 articles.

1. Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2015. Neural machine translation by jointly learning to align and translate. ICLR ( 2015 ). Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. ICLR (2015).

2. Geometric Deep Learning: Going beyond Euclidean data

3. Synthesized Classifiers for Zero-Shot Learning

4. Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning

5. Multi-modal Siamese Network for Entity Alignment

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