Feng Zhang

Orcid: 0000-0002-8373-9366

Affiliations:
  • Peking University, Beijing, China


According to our database1, Feng Zhang authored at least 20 papers between 2021 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
ReranKGC: A cooperative retrieve-and-rerank framework for multi-modal knowledge graph completion.
Neural Networks, 2025

SEP-MLDC: A Simple and Effective Paradigm for Multi-Label Document Classification.
Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2025, Albuquerque, New Mexico, USA, April 29, 2025

SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models.
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.1, 2025

PR-KGC: Text-enhanced Knowledge Graph Completion with Pair-wise Re-ranking.
Proceedings of the 2025 IEEE International Conference on Acoustics, 2025

Clear Up Confusion: Iterative Differential Generation for Fine-grained Intent Detection with Contrastive Feedback.
Proceedings of the 31st International Conference on Computational Linguistics, 2025

AdaDHP: Fine-Grained Fine-Tuning via Dual Hadamard Product and Adaptive Parameter Selection.
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

Multi-Label Few-Shot Image Classification via Pairwise Feature Augmentation and Flexible Prompt Learning.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024
A Coarse-to-Fine Prototype Learning Approach for Multi-Label Few-Shot Intent Detection.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

Meta-Prompt Tuning Vision-Language Model for Multi-Label Few-Shot Image Recognition.
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

From Discrimination to Generation: Low-Resource Intent Detection with Language Model Instruction Tuning.
Proceedings of the Findings of the Association for Computational Linguistics, 2024

SELP: A Semantically-Driven Approach for Separated and Accurate Class Prototypes in Few-Shot Text Classification.
Proceedings of the Findings of the Association for Computational Linguistics, 2024

Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor Generation and Classification Reframing.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Depression Detection via Capsule Networks with Contrastive Learning.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Dual Class Knowledge Propagation Network for Multi-label Few-shot Intent Detection.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

Boosting Few-Shot Text Classification via Distribution Estimation.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

SSPAttack: A Simple and Sweet Paradigm for Black-Box Hard-Label Textual Adversarial Attack.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism.
Proceedings of the SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11, 2022

Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021


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