본 과제를 통해 발표한 논문입니다.

클라우드 로봇 지능

  • 장민수, 김도형, & 김재홍. (2020). 서비스 로봇의 실환경 적용을 위한 클라우드 로봇 지능. 로봇과 인간, 17(3), 15-20.

맥락 이해

Personal Attribute Recognition

  • Yoon, H. S., Park, S. W., & Yoo, J. H. (2021). Real-time hair segmentation using mobile-unet. Electronics, 10(2), 99.
  • Jang, J., Yoon, H. S., & Kim, J. (2021). Improvement of identity recognition with occlusion detection-based feature selection. Electronics, 10(2), 167.
  • Jeong, B., Park, J., & Kwak, S. (2021). Asmr: Learning attribute-based person search with adaptive semantic margin regularizer. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 12016-12025).

Grounded Situation Recognition

  • Cho, J., Yoon, Y., & Kwak, S. (2022). Collaborative Transformers for Grounded Situation Recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 19659-19668).
  • Kim, N., Kim, D., Lan, C., Zeng, W., & Kwak, S. (2022). ReSTR: Convolution-free Referring Image Segmentation Using Transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 18145-18154).
  • Cho, J., Yoon, Y., Lee, H., & Kwak, S. (2021). Grounded Situation Recognition with Transformers. In Proceedings of the 32nd British Machine Vision Conference.

Group Activity Recognition

  • Kim, D., Lee, J., Cho, M., & Kwak, S. (2022). Detector-Free Weakly Supervised Group Activity Recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 20083-20093).

Crowd Analysis

  • Kim, N., & Kwak, S. (2021). Robust Crowd Counting via Image Enhancement and Dynamic Feature Selection. In Proceedings of the 32nd British Machine Vision Conference.

서비스 개인화와 특화

Service Personalization and User Adaptation

  • Gasteiger, N., Hellou, M., & Ahn, H. S. (2021). Factors for personalization and localization to optimize human–robot interaction: A literature review. International Journal of Social Robotics, 1-13.
  • Hellou, M., Gasteiger, N., & Ahn, H. S. (2021, November). Personalization and Localization to Improve Social Robots’ Behaviors: A Literature Review. In International Conference on Social Robotics (pp. 763-767). Springer, Cham.
  • Hellou, M., Gasteiger, N., Lim, J. Y., Jang, M., & Ahn, H. S. (2021). Personalization and Localization in Human-Robot Interaction: A Review of Technical Methods. Robotics, 10(4), 120.
  • Gasteiger, N., Hellou, M., & Ahn, H. S. (2021, July). Optimizing human-robot interaction through personalization: an evidence-informed guide to designing social service robots. In 2021 18th International Conference on Ubiquitous Robots (UR) (pp. 53-56). IEEE.
  • Grover, I., Huggins, M., Breazeal, C., & Park, H. W. (2021, November). MRF-Chat: Improving Dialogue with Markov Random Fields. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 4925-4936).
  • Kim, U. H., Hwang, Y., Lee, S. K., & Kim, J. H. (2022). Writing in The Air: Unconstrained Text Recognition from Finger Movement Using Spatio-Temporal Convolution. IEEE Transactions on Artificial Intelligence.
  • Lee, S. K., & Kim, J. H. (2021, October). Air-Text: Air-Writing and Recognition System. In Proceedings of the 29th ACM International Conference on Multimedia (pp. 1267-1274).

지역 적응

Weakly-Supervised Learning

  • Kim, N., Hwang, S., & Kwak, S. (2022). Learning to Detect Semantic Boundaries with Image-Level Class Labels. International Journal of Computer Vision, 130(9), 2131-2148.

Domain Adaptation

  • Yun, W. H., Kim, T., Lee, J., Kim, J., & Kim, J. (2021). Cut-and-Paste Dataset Generation for Balancing Domain Gaps in Object Instance Detection. IEEE Access, 9, 14319-14329.
  • Yun, W. H., Han, B., Lee, J., Kim, J., & Kim, J. (2021). Target-style-aware unsupervised domain adaptation for object detection. IEEE Robotics and Automation Letters, 6(2), 3825-3832.
  • Hwang, S., Lee, S., Kim, S., Ok, J., & Kwak, S. (2022). Combating Label Distribution Shift for Active Domain Adaptation. In European Conference on Computer Vision (pp. 549-566). Springer, Cham.

전역 지능 적용

Transfer Learning

  • Kim, S., Kim, D., Cho, M., & Kwak, S. (2021). Embedding transfer with label relaxation for improved metric learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3967-3976).

Knowledge Distillation

  • Seo, M., Lee, Y., & Kwak, S. (2021, December). On the distribution of penultimate activations of classification networks. In Uncertainty in Artificial Intelligence (pp. 1141-1151). PMLR.

서비스와 기술 실증

  • Hellou, M., Lim, J., Gasteiger, N., Jang, M., & Ahn, H. S. (2022). Technical Methods for Social Robots in Museum Settings: An Overview of the Literature. International Journal of Social Robotics, 1-20.
  • Kim, M. G., Park, M., Kim, J., Kwon, Y. S., Sohn, D. S., Yoon, H., & Seo, K. H. (2021, March). On the common and different expectations on robot service in restaurant between customers and employees. In Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (pp. 262-265).
  • Kim, M. G., Yoon, H., Kim, J., Kim, J., Sohn, D. S., & Kim, K. (2021, July). Investigating Frontline Service Employees to Identify Behavioral Goals of Restaurant Service Robot: An Exploratory Study. In 2021 18th International Conference on Ubiquitous Robots (UR) (pp. 57-62). IEEE.
  • Park, J., Kim, J., Kim, D. Y., Kim, J., Kim, M. G., Choi, J., & Lee, W. (2022, March). User Perception on Personalized Explanation by Science Museum Docent Robot. In 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (pp. 973-975). IEEE.
  • Cho, M., Jang, J., Lee, J., Jang, M., Kim, D., & Kim, J. (2022, August). Real-world Validation Study of Daily Activity Detection for the Elderly. In 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) (pp. 1341-1345). IEEE.
  • Kim, T., Jang, M., & Kim, J. (2021, July). A Survey on Simulation Environments for Reinforcement Learning. In 2021 18th International Conference on Ubiquitous Robots (UR) (pp. 63-67). IEEE.