Hwanjun Song

KAIST
Assistant Professor

E2-3110, 291 Daehak-ro, Yuseong-gu
Daejeon 34141, Republic of Korea

Email: songhwanjunxkxkxk@kaist.ac.kr / ghkswns91xkxkxk@gmail.com

Welcome to my page! I am an Assistant Professor at KAIST in the Graduate School of Data Science (GSDS) and the Department of Industrial and Systems Engineering (ISyse). Previously, I was a Research Scientist at Amazon Web Services Inc. (AWS AI Labs, Seattle), and NAVER Corp. (NAVER AI Lab, Seungnam). I also earned my PhD in February 2021 from the Graduate School of Knowledge Service Engineering (currently, Data Science) at KAIST and worked as a research intern at Google Research, where I was fortunate to work with two supervisors, Prof. Jae-Gil Lee and Prof. Ming-Hsuan Yang. My current research interests lie in exploring the cutting-edge technologies for future AI (e.g., Data Robust/Efficient Learning, Transformers, Large Language Models) and improving the performance of AI systems under real-world scenarios related to data scale and quality. News

May 2024: A research paper on Open-vocabulariy Object Detection with Transformers was accepted at the Journal of Pattern Recognition (SCIE, IF=8.0, Q1).

May 2024: A research paper on Quantization of Hybrid Vision Transformers was accepted at the IEEE Internet of Things Journal (SCIE, IF=10.6, Q1).

May 2024: Two research papers on an automatic evaluator using LLMs and intent encoders were accepted at the main track of ACL 2024 (long paper).

May 2024: A research paper on continual learning was accepted at ICML 2024.

Mar 2024: Three research papers on multimodal data creation, hallucination evaluation, semi-supervised text summarization was accepted at the main track of NAACL.

Jan 2024: We received a silver prize at the Samsung Humantech Paper Awards (Signal Processing/NLP).

Jan 2024: A Paper on 'Time-series Anomaly Detection' accepted at TheWebConf (WWW) 2024.

Jan 2024: Papers on 'Noisy Labels' and 'Continual Learning' accepted at AAAI 2024.

See our Lab Webpage.

Publications Google scholar profile An underline indicates the corresponding author.

2024

	H. Song, J. Bang. Prompt-Guided DETR with RoI-Pruned Masked Attention for Open-Vocabulary Object Detection. Pattern Recognition (SCIE, IF=8.0, Q1) 2024.
	J. Lee, Y. Kwon, S. Park, M. Yu, J. Park, H. Song H. Song, S. Mansour. Q-HyViT: Post-Training Quantization of Hybrid Vision Transformers with Bridge Block Reconstruction for IoT Systems. Internet of Things Journal (SCIE, IF=10.6, Q1) 2024.
	H. Song, H. Su, I. Shalyminov, J. Cai, S. Mansour. FineSurE: Fine-grained Summarization Evaluation using LLMs. Annual Meeting of the Association for Computational Linguistics (ACL-main) 2024.
	Y. Zhang, S. Singh, S. Sengupta, I. Shalyminov, H. Su, H. Song, S. Mansour. Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders . Annual Meeting of the Association for Computational Linguistics (ACL-main) 2024.
	D. Kim, S. Yoon, D. Park, Y. Lee, H. Song, JG. Lee. One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning. International Conference on Machine Learning (ICML) 2024.
	H. Aboutalebi, H. Song, Y. Xie, A. Gupta, J. Sun, H. Su, I. Shalyminov, N. Pappas, S. Signh, S. Mansour. MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-main) 2024. [pdf]
	L. Tang, I. Shalyminov, A. W. Wong, J. Burnsky, J. W Vincent, Y. Yang, S. Singh, S. Feng, H. Song, H. Su, L. Sun, Y. Zhang, S. Mansour, K. McKeown. TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-main) 2024. [pdf]
	J. He, H, Su, J. Cai, I, Shalyminov, H. Song, S. Mansour. Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-main) 2024.
	Y. Nam, S. Yoon, Y. Shin, M. Bae, H. Song, J.G. Lee, BS. Lee M. Breaking the Time-Frequency Granularity Discrepancy in Time-Series Anomaly Detection. TheWebConf (WWW) 2024. To Appear
	H. Song, M. Kim, JG. Lee. Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise. The AAAI Conference on Artificial Intelligence (AAAI) 2024. [pdf]
	D. Kim, D. Park, Y. Shin, H. Song, JG. Lee. Adaptive Shortcut Debiasing for Online Continual Learning. The AAAI Conference on Artificial Intelligence (AAAI) 2024.

2023

	H. Song, I. Shalyminov, H. Su, S. Singh, K. Yao, S. Mansour. Enhancing Abstractiveness of Summarization Models through Calibrated Distillation. International Conference on Empirical Methods in Natural Language Processing (EMNLP Findings) 2023. [Amazon Science Blog]
	S. Bae, J. Ko, H. Song, SY. Yun. Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding. International Conference on Empirical Methods in Natural Language Processing (EMNLP main) 2023.
	D. Park, S. Choi, D. Kim, H. Song, JG. Lee. Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy. Annual Conference on Neural Information Processing Systems (NeurIPS) 2023. To Appear
	J. Lee, Y. Kwon, J. Park, M. Yu, S. Park, H. Song Q-HyViT: Post-Training Quantization for Hybrid Vision Transformer with Bridge Block Reconstruction. Preprint arXiv 2023. [pdf]
	D. Jung, D. Han, J. Bang, H. Song. Generating Instance-level Prompts for Rehearsal-free Continual Learning. International Conference on Computer Vision (ICCV) 2023. Oral Presentation.
	Y. Shin, S. Yoon, H. Song, D. Park, B. Kim, JG. Lee, BS. Lee. Context Consistency Regularization for Label Sparsity in Time Series. International Conference on Machine Learning (ICML) 2023. [pdf]
	H. Song, J. Bang. Prompt-Guided Transformers for End-to-End Open-Vocabulary Object Detection. Preprint arXiv 2023. [pdf] [code]
	S. Kim, S, Bae, H. Song, SY. Yun. Re-thinking Federated Active Learning based on Inter-class Diversity. International Conference on Computer Vision and Pattern Recognition (CVPR) 2023. [pdf]
	H. Koh, M. Seo, J. Bang, H. Song, D. Hong, S. Park, JW. Ha, J. Choi. Online Boundary-Free Continual Learning by Scheduled Data Prior. International Conference on Learning Representations (ICLR) 2023 . [pdf]
	S. E. Whang, Y. Roh, H. Song, JG. Lee. Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective. The VLDB Journal 2023. [pdf]

2022

	D. Park, Y. Shin, J. Bang, Y. Lee, H. Song, JG. Lee. Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning. Annual Conference on Neural Information Processing Systems (NeurIPS) 2022. [pdf] [code]
	J. Oh, S. Kim, N. Ho, JH. Kim, H. Song, SY. Yun. Understanding Cross-domain Few-shot Learning: An Experimental Study. Annual Conference on Neural Information Processing Systems (NeurIPS) 2022. [pdf] [code]
	D. Park, J. Kang, H. Song, S. Yoon, JG Lee. Multi-view POI-level Cellular Trajectory Reconstruction for Digital Contact Tracing of Infectious Diseases. International Conference on Data Minig (ICDM) 2022.
	S. Kim, W. Shin, S. Jang, H. Song, SY. Yun. FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning. International Conference on Information and Knowledge Management (CIKM) 2022. [pdf]
	W. Shin, J. Park, T. Woo, Y. Cho, K. Oh, H. Song. e-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce. International Conference on Information and Knowledge Management (CIKM) 2022. The first large-scale industry study investigating a unified multimodal transformer model. [pdf]
	J. Oh, S. Kim, N. Ho, JH. Kim, H. Song, SY. Yun. ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning. International Conference on Information and Knowledge Management (CIKM) 2022. This paper was also presented at ICML UpML workshop, 2022. [pdf]
	H. Song, D. Sun, S. Chun, V. Jampani, D. Han, B. Heo, W. Kim, and M-H Yang. An Extendable, Efficient and Effective Transformer-based Object Detector. Preprint arXiv 2022. An extended version of ViDT to support multi-task learning. [pdf] [code]
	S. Kim, S. Bae, H. Song, SY. Yun. LG-FAL: Federated Active Learning Strategy using Local and Global Models. International Conference on Machine Learning (ReALML Workshop) 2022.
	S. Yun, J. Kim, D. Han, H. Song, JW. Ha, J. Shin. Time Is MattEr: Temporal Self-supervision for Video Transformers. International Conference on Machine Learning (ICML) 2022. Short Presentation.
	JH. Kim, J. Kim, SJ. Oh, S. Yun, H. Song, J. Jeong, JW. Ha, HO. Song. Dataset Condensation via Efficient Synthetic-Data Parameterization. International Conference on Machine Learning (ICML) 2022. Short Presentation. [pdf] [code]
	J. Bang, H. Koh, S. Park, H. Song, JW. Ha, J. Choi. Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries. International Conference on Computer Vision and Pattern Recognition (CVPR) 2022. [pdf] [code]
	H. Song, M. Kim, D. Park, Y. Shin, JG. Lee. Learning from Noisy Labels with Deep Neural Networks: A Survey. IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2022. The most cited survey paper on handling noisy labels with DNNs. [pdf] [code]
	H. Song, D. Sun, S. Chun, V. Jampani, D. Han, B. Heo, W. Kim, and M-H Yang. ViDT: An Efficient and Effective Fully Transformer-based Object Detector. International Conference on Learning Representations (ICLR) 2022. [pdf] [code]
	Y. Shin, S. Yoon, S. Kim, H. Song, JG. Lee, B. S. Lee. Coherence-based Label Propagation over Time Series for Accelerated Active Learning. International Conference on Learning Representations (ICLR) 2022. [pdf] [code]
	M. Kim, H. Song, Y. Shin, D. Park, K. Shin, JG. Lee. Meta-Learning for Online Update of Recommender Systems. The AAAI Conference on Artificial Intelligence (AAAI) 2022. [pdf]
	D. Kim, H. Min, Y. Nam, H. Song, S. Yoon, M. Kim, JG. Lee. COVID-EENet: Predicting Fine-Grained Impact of COVID-19 on Local Economies . The AAAI Conference on Artificial Intelligence (AAAI) 2022. Oral Presentation. [pdf] [code]

2021

	H. Song, E. Kim, V. Jampani, D. Sun, M-H. Yang. Exploiting Scene Depth for Object Detection with Multimodal Transformers. British Machine Vision Conference (BMVC) 2021. [pdf] [code]
	D. Park, H. Song, M. Kim, JG. Lee. Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data. Annual Conference on Neural Information Processing Systems (NeurIPS) 2021. [pdf] [code]
	H. Song, M. Kim, D. Park, Y. Shin, JG. Lee. Robust Learning by Self-Transition for Handling Noisy Labels. International Conference on Knowledge Discovery and Data Mining (KDD) 2021. Oral Presentation. [pdf]
	JG. Lee, Y. Roh, S. E. Whang. Machine Learning Robustness, Fairness, and their Convergence. . International Conference on Knowledge Discovery and Data Mining (KDD) 2021. 2+ Hour Live Tutorial. [webpage] [pdf] [video] [slide]
	M. Kim, H. Song, D. Kim, K. Shin, JG. Lee. PREMERE: Meta-Reweighting via Self-Ensembling for Point-of-Interest Recommendation. The AAAI Conference on Artificial Intelligence (AAAI) 2021. Oral Presentation. [pdf] [code]

2020

	H. Song, M, Kim, S. Kim, JG. Lee. Carpe Diem, Seize the Samples Uncertain "At the Moment" for Adaptive Batch Selection. International Conference on Information and Knowledge Management (CIKM) 2020. Oral Presentation. [pdf] [code]
	H. Song, S. Kim, M. Kim, JG. Lee. Ada-Boundary: Accelerating DNN Training via Adaptive Boundary Batch Selection. Machine Learning (ML) 2020. Invited Paper and Oral Presentation at ECML-PKDD 2020. [pdf] [code]
	M. Kim, J. Kang, Dim, H. Song, H. Min, Y. Nam, D. Park, JG. Lee. Hi-COVIDNet: Deep Learning Approach to Predict Inbound COVID-19 Patients and Case Study in South Korea . International Conference on Knowledge Discovery and Data Mining (KDD) 2020. Oral Presentation. [pdf] [code]
	H. Song, M. Kim, D. Park, JG. Lee. How Does Early Stopping Help Generalization against Label Noise? . International Conference on Machine Learning (UDL Workshop) 2020. [pdf] [code]
	S. Kim, H. Song, S. Kim, B. Kim, JG. Lee. Revisit Prediction by Deep Survival Analysis. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2020. [pdf]
	D. Park, H. Song, M. Kim, JG. Lee. TRAP: Two-level Regularized Autoencoder-based Embedding for Power-law Distributed Data. TheWebConf (WWW) 2020. Oral Presentation. [pdf] [code]

2019

	D. Park, S. Yoon, H. Song, JG. Lee. MLAT: Metric Learning for kNN in Streaming Time Series. International Conference on Knowledge Discovery and Data Mining (MileTs Workshop) 2019. [pdf]
	H. Song, M. Kim, JG. Lee. SELFIE: Refurbishing Unclean Samples for Robust Deep Learning. International Conference on Machine Learning (ICML) 2019. Short Presentation. [pdf] [code] [dataset]

2018

H. Song, JG. Lee. RP-DBSCAN: A Superfast Parallel DBSCAN Algorithm based on Random Partitioning. International Conference on Management of Data (SIGMOD) 2018. Top 2% of the submitted papers (accepted without revision round). Oral Presentation. [pdf] [code]

2017

H. Song, JG. Lee, WS. Han. PAMAE: Parallel k-Medoids Clustering with High Accuracy and Efficiency. International Conference on Knowledge Discovery and Data Mining (KDD) 2017. Selected one of the outstanding research among Microsoft Azure Supporting Projects. [blog] [pdf] [code]

Services

Co-organized Workshop on Machine Learning Robustness, Fairness, and their Convergence at KDD 2021

Reviewer for ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV, PAMI, IJCV, TNNLS since 2020

Seminar / Techtalk

Data Robustness and Efficiency, Vision Transformers, and Continual Learning, GIST and IEEE Seminar, Dec 2022 [slides]
ML Robustness against Label Noise, UNIST Graduate School of AI, Sep 2022
Robust Deep Learning and Extension to Real-world Applications, Database Society Summer School, Aug 2022
An Extendable, Efficient and Effective Tranformer-based Object Detector, NAVER CLOVA, Jun 2022
ML Robustness against Label Noise, Amazon AWS AI / Responsible AI, Mar 2022
Transformers for Computer Vision, Electronics and Telecommunications Research Institute, Feb 2022
Machine Learning Robustness, Fairness, and their Convergence, KDD Tutorial, Aug 2021
Robust Learning by Self-transition for Handling Noisy Labels, NAVER CLOVA, May 2021
Learning from Noisy Labels for Classification, Google Research, May 2021
Exploiting Scene Depth for Object Detection, Google Research & NAVER AI Lab, Dec 2020
Robust Learning under Label Noise, Institute of Basic Science, Dec 2019
Parallel Clustering and Large-Scale Data Analytics, NAVER CLOVA, Aug 2018

Students and Interns

I have been fortunate to work with many gifted students:

Yuwei Zhang (UC San Diego), 2023

Hossein Aboutalebi (University of Waterloo), 2023

Jianfeng He (Virginia Tech), 2023

Chaoyi Zhang (University of Sydney), 2023

Minseok Kim (KAIST → Amazon Alexa), 2019-2022

Dongmin Park (KAIST), 2020-2022

Yooju Shin (KAIST), 2020-2022

Doyoung Kim (KAIST), 2020-2022

Sangmook Kim (KAIST), 2021-2022

Jaehoon Oh (KAIST), 2021-2022

Seulki Park (Seoul National University), 2021-2022

Saehyung Lee (Seoul National University), 2022

Sangmin Bae (KAIST), 2022

Dahuin Jung (Seoul National University), 2022