diff --git a/_news/announcement_1.md b/_news/announcement_1.md index aa0f3b5..861dadc 100644 --- a/_news/announcement_1.md +++ b/_news/announcement_1.md @@ -1,19 +1,20 @@ --- layout: post -title: I am accepted as the final recipient of the Hyundai Global Fellowship program! +title: Congratulations! I am accepted as the final recipient of the Hyundai Global Fellowship program! date: 2022-01-27 16:40:00 description: Hyundai Motor Chung Mong-koo Scholarship tags: formatting links categories: sample-posts inline: false --- -I am pleased to announce that I am selected as the final recipient of the Hyundai Global Fellowship program. + +I am pleased to announce that I am selected as the final recipient of the Hyundai Global Fellowship program. This scholarship has a big coverage. - Full tuition fee (including entrance fee) -- learning support fee (1,000,000won per month) -- settlement allowance (only 1 time when I enter the school) -- graduation celebration incentive (only 1 time when graduate). +- Learning support fee (1,000,000won per month) +- Settlement allowance (only 1 time when I enter the school) +- Graduation celebration incentive (only 1 time when graduate). In the 2023 Spring semester, I will pursue a Master’s degree in Computer Engineering at Korea University, Korea, with full support from this scholarship! diff --git a/_news/announcement_2.md b/_news/announcement_2.md index e2b2086..22b66f8 100644 --- a/_news/announcement_2.md +++ b/_news/announcement_2.md @@ -1,8 +1,9 @@ --- layout: post -title: (Accepted) Improving Human-object Interaction with Auxiliary Semantic Information and Enhanced Instance Representation +title: Congratulations! Improving Human-object Interaction with Auxiliary Semantic Information and Enhanced Instance Representation has been accepted in Pattern Recognition Letters (IF = 5.1) date: 2023-09-26 00:00:00 -description: (Accepted) Improving Human-object Interaction with Auxiliary Semantic Information and Enhanced Instance Representation +description: Congratulations! Improving Human-object Interaction with Auxiliary Semantic Information and Enhanced Instance Representation has been accepted in Pattern Recognition Letters (IF = 5.1) + tags: formatting links categories: sample-posts inline: false @@ -26,4 +27,4 @@ We will publish our source code and checkpoints for this study soon! **P/s 2**: We all know that papers are just the results of researching period, and they are not that all, they are just some milestones. We should continue to try our best in the academic career, to bring not only theorical but also practical research to the community. There is a long road to go for achieving this. -Thank you for reading! +Thank you for reading! \ No newline at end of file diff --git a/_news/announcement_3.md b/_news/announcement_3.md index 840dcfd..c219337 100644 --- a/_news/announcement_3.md +++ b/_news/announcement_3.md @@ -9,3 +9,5 @@ inline: false --- Our collaborative project titled "UIT-OpenViIC: An Open-domain Benchmark for Evaluating Image Captioning in Vietnamese" is currently under review for the first round. The paper involves the joint efforts of BSc. Nghia Hieu Nguyen. + + \ No newline at end of file diff --git a/_news/announcement_4.md b/_news/announcement_4.md index 11f143b..f5c21b0 100644 --- a/_news/announcement_4.md +++ b/_news/announcement_4.md @@ -1,13 +1,17 @@ --- layout: post -title: (Under review) DAX-Net - a dual-branch dual-task adaptive cross-weight feature fusion network for robust multi-class cancer classification in pathology images -date: 2023-07-04 13:50:00 -description: (Under review) DAX-Net - a dual-branch dual-task adaptive cross-weight feature fusion network for robust multi-class cancer classification in pathology images +title: Congratulations! Our paper "DAX-Net - a dual-branch dual-task adaptive cross-weight feature fusion network for robust multi-class cancer classification in pathology images" has been accepted in Computer Methods and Programs in Biomedicine (IF = 6.1) +date: 2024-03-02 00:00:00 +description: Congratulations! Our paper "DAX-Net - a dual-branch dual-task adaptive cross-weight feature fusion network for robust multi-class cancer classification in pathology images" has been accepted in Computer Methods and Programs in Biomedicine (IF = 6.1) tags: formatting links categories: sample-posts inline: false --- -D.C. Bui, B. Song, K. Kim, J.T. Kwak, DAX-Net: a dual-branch dual-task adaptive cross-weight feature fusion network for robust multi-class cancer classification in pathology images, , 2023, . Paper ( Under review ) +We are pleased to announce that our paper titled 'DAX-Net: A Dual-Branch Dual-Task Adaptive Cross-Weight Feature Fusion Network for Robust Multi-Class Cancer Classification in Pathology Images' has been accepted in Computer Methods and Programs in Biomedicine (IF = 6.1). -Update: After a year, we have the review comments for this paper in first round review. Started working on the revised manuscript. Not sure what will happen, but hope the best. + + +Additionally, this marks my first journal submission accepted during my master's studies in Korea. + +The code, manuscript, and datasets will be released soon! \ No newline at end of file diff --git a/_news/announcement_5.md b/_news/announcement_5.md index bfeca53..d658444 100644 --- a/_news/announcement_5.md +++ b/_news/announcement_5.md @@ -20,4 +20,4 @@ Meet our dedicated team: Furthermore, we are honored to share that our paper presenting this groundbreaking solution, titled "C2T-Net: Cross-Fused Transformer-Style Networks for Pedestrian Attribute Recognition," has been accepted, and we will be delivering an oral presentation at the WACV2024-RWS Workshop. This recognition underscores the innovation and impact of our work in advancing the field of pedestrian attribute recognition. -Please check our published source code [here](https://github.com/caodoanh2001/upar_challenge). +Please check our published source code [here](https://github.com/caodoanh2001/upar_challenge). \ No newline at end of file diff --git a/_pages/about.md b/_pages/about.md index 047bfbb..8b2e96f 100644 --- a/_pages/about.md +++ b/_pages/about.md @@ -3,14 +3,13 @@ layout: about title: about permalink: / subtitle: University of Information Technology. Ho Chi Minh city, Vietnam. -subtitle: QuilL, School of Electrical and Engineering, Korea University. Seoul, Republic of Korea. +subtitle: QuIIL, School of Electrical and Engineering, Korea University. Seoul, Republic of Korea. profile: align: right image: prof_pic.jpg image_circular: false # crops the image to make it circular address: > -

Thu Duc city, Ho Chi Minh city, Vietnam

Seoul, Republic of Korea

news: true # includes a list of news items @@ -18,7 +17,7 @@ selected_papers: true # includes a list of papers marked as "selected={true}" social: true # includes social icons at the bottom of the page --- -I am Doanh C. Bui, a Bachelor of Computer Science graduate from the University of Information Technology (UIT), VNU-HCM. I completed my degree in September 2022. For a brief period, from November 2022 to February 2023, I served as a teaching assistant at the Faculty of Software Engineering, UIT. Currently, I am pursuing a master's degree in the School of Electrical Engineering at Korea University, where I am in my second semester. I am fortunate to be under the guidance of Prof. Jin Tae Kwak. +I am Doanh C. Bui, a Bachelor of Computer Science graduate from the University of Information Technology (UIT), VNU-HCM. I completed my degree in September 2022. For a brief period, from November 2022 to February 2023, I served as a teaching assistant at the Faculty of Software Engineering, UIT. Currently, I am pursuing a master's degree in the School of Electrical Engineering at Korea University, where I am in my third semester, under the supervision of Prof. Jin Tae Kwak. Throughout my academic journey, I have focused on various aspects of Computer Vision, specifically in areas such as Object Detection, Document Image Understanding, Image Captioning, and Human-Object Interaction. Presently, my research revolves around leveraging image processing techniques for histopathology images. diff --git a/_posts/2023-07-08-faster-rcnn.md b/_posts/2023-07-08-faster-rcnn.md index e7b9178..2825027 100644 --- a/_posts/2023-07-08-faster-rcnn.md +++ b/_posts/2023-07-08-faster-rcnn.md @@ -23,7 +23,7 @@ Vào thời điểm trước khi nhóm phương pháp R-CNN ra đời, các phư ![](https://i.imgur.com/wxFZHHH.png) Hình 1. Minh họa phương pháp R-CNN [1]. -Phương pháp này có thể được mô tả đơn giản như sau: đầu tiên thuật toán **selective search** sẽ chọn ra khoảng $N$ vùng trên ảnh có khả năng cao chứa đối tượng. Thuật toán này chủ yếu dựa vào các đặc điểm bức ảnh như màu sắc. Trong bài báo gốc, các tác giả sử dụng $N = 2000$, tức sẽ có $2000$ vùng được đề xuất trên ảnh. Từ 2000 vùng này, ta sẽ tiến hành cắt ra từ ảnh gốc, và một mạng CNN sẽ được sử dụng để trích xuất đặc trưng của 2000 vùng ảnh này. Sau đó, từ lớp đặc trưng cuối cùng sẽ đi qua 1 lớp FC để tính toán một bộ offset $(\delta x, \delta y, \delta w, \delta h)$, trong đó $(\delta x, \delta y)$ là offset tọa độ tâm của đối tượng, $(\delta w, \delta h)$ là offset chiều rộng và chiều cao của đối tượng. Như vậy, mạng sẽ học cách bo sát đối tượng và phân lớp đối tượng từ N vùng truyền vào ban đầu. Đối tượng sẽ được phân lớp bằng thuật toán SVM (Support Vector Machine). +Phương pháp này có thể được mô tả đơn giản như sau: đầu tiên thuật toán **selective search** sẽ chọn ra khoảng $N$ vùng trên ảnh có khả năng cao chứa đối tượng. Thuật toán này chủ yếu dựa vào các đặc điểm bức ảnh như màu sắc. Trong bài báo gốc, các tác giả sử dụng $$N = 2000$$, tức sẽ có $2000$ vùng được đề xuất trên ảnh. Từ 2000 vùng này, ta sẽ tiến hành cắt ra từ ảnh gốc, và một mạng CNN sẽ được sử dụng để trích xuất đặc trưng của 2000 vùng ảnh này. Sau đó, từ lớp đặc trưng cuối cùng sẽ đi qua 1 lớp FC để tính toán một bộ offset $(\delta x, \delta y, \delta w, \delta h)$, trong đó $(\delta x, \delta y)$ là offset tọa độ tâm của đối tượng, $(\delta w, \delta h)$ là offset chiều rộng và chiều cao của đối tượng. Như vậy, mạng sẽ học cách bo sát đối tượng và phân lớp đối tượng từ N vùng truyền vào ban đầu. Đối tượng sẽ được phân lớp bằng thuật toán SVM (Support Vector Machine). > Q: offset là gì? diff --git a/assets/img/daxnet.jpg b/assets/img/daxnet.jpg new file mode 100644 index 0000000..2ba7da5 Binary files /dev/null and b/assets/img/daxnet.jpg differ diff --git a/assets/img/uit-openviic.jpg b/assets/img/uit-openviic.jpg new file mode 100644 index 0000000..e1c96bd Binary files /dev/null and b/assets/img/uit-openviic.jpg differ