Chia-Wen Kuo (郭佳文)

Research Focus: Vision and Language; Knowledge Augmentation; Semi-Supervised Learning; Self-Supervised Learning; Zero-Shot Learning

Contact: albert.cwkuo[AT]gatech.edu

Chia-Wen Kuo (郭佳文) has successfully completed his Ph.D. at Georgia Tech and is now embarking on a new journey as a research scientist at ByteDance. He joined the RobotIcs Perception and Learning (RIPL) lab in 2017, led by Dr. Zsolt Kira. During his Ph.D., Chia-Wen focused on deep learning in the field of vision-and-language (VL) research. His Ph.D. dissertation was centered on developing efficient and effective ways to integrate external knowledge into VL models and tasks.

Prior to joining Georgia Tech, he worked with Dr. Yu-Chiang Frank Wang on 3D shape generation and pose estimation. Before that, he earned his Master's and Bachelor's degrees in Electrical Engineering at National Taiwan University.

Google Scholar

News

(02/26/2024) Research scientist at ByteDance
(12/15/2023) 👨‍🎓 Graduate from Georgia Tech
(11/29/2023) 🎓 Pass Ph.D. Defense
(07/10/2023) Code release for HAAV [GitHub]
(06/18-22/2023) Attend CVPR-23 in Vancouver
(04/18/2023) 🎓 Pass Ph.D. Proposal Exam
(02/27/2023) 📜 HAAV paper accepted in CVPR-23

Publications

HAAV: Hierarchical Aggregation of Augmented Views for Image Captioning

[CVPR 2023] Chia-Wen Kuo and Zsolt Kira

We propose HAAV to efficiently and effectively leverage multiple sources of knowledge for image captioning.

[Paper] [GitHub] [Project] [Video]

Beyond a Pre-Trained Object Detector: Cross-Modal Textual and Visual Context for Image

[CVPR 2022] Chia-Wen Kuo and Zsolt Kira

We propose Xmodal-Ctx to incorporate text descriptions of the input image via CLIP cross-modal retrieval for image captioning.

[Paper] [GitHub] [Project] [Video]

Structure-Encoding Auxiliary Tasks for Improved Visual Representation in Vision-and-Language Navigation

[WACV 2023] Chia-Wen Kuo, Chih-Yao Ma, Judy Hoffman, Zsolt Kira

We propose SEA to improve the visual representation through a series of self-supervised tasks for vision-and-language navigation.

[Paper] [GitHub] [Project]

Unbiased Teacher for Semi-Supervised Object Detection

[ICLR 2021] Yen-Cheng Liu, Chih-Yao Ma, Zijian He, Chia-Wen Kuo, Kan Chen, Peizhao Zhang, Bichen Wu, Zsolt Kira, Peter Vajda

We propose Unbiased Teacher using focal loss and teacher-student training to reduce the bias in pseudo-labeling for semi-supervised object detection.

[arXiv] / [GitHub] / [Project]

FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning

[ECCV 2020] Chia-Wen Kuo, Chih-Yao Ma, Jia-Bin Huang, Zsolt Kira

We propose FeatMatch using feature-based augmentation for semi-supervised image classification.

[arXiv] / [GitHub] / [Project]

Who2com: Collaborative Perception via Learnable Handshake Communication

[ICRA 2020] Yen-Cheng Liu, Junjiao Tian, Chih-Yao Ma, Nathaniel Glaser, Chia-Wen Kuo, Zsolt Kira

[arXiv] / [Project]

Data-Efficient Graph Embedding Learning for PCB Component Detection

[WACV 2019] Chia-Wen Kuo, Jacob D. Ashmore, David Huggins, Zsolt Kira

We improve few-shot PCB component detection by leveraging the similarity relationship between detected components and component prototypes via graph neural network.

[arXiv] / [Project]