47 citations · 52 across the 6 of their papers we have counts for
6 papers
What You Ask is What You Ground: Bridging Question Intent to Temporal Evidence for Grounded VideoQA
Jinhwan Seo, Kyubeom Han, Jumin Lee +2
We study a critical yet overlooked failure mode in Grounded Video Question Answering: question-invariant grounding, where models predict nearly identical temporal segments for diff…
Radiometrically Consistent Gaussian Surfels for Inverse Rendering
Kyu Beom Han, Jaeyoon Kim, Woo Jae Kim +2
Inverse rendering with Gaussian Splatting has advanced rapidly, but accurately disentangling material properties from complex global illumination effects, particularly indirect ill…
Pinpointing Trigger Moment for Grounded Video QA: Enhancing Spatio-temporal Grounding in Multimodal Large Language Models
Jinhwan Seo, Yoonki Cho, Junhyug Noh +1
In this technical report, we introduce a framework to address Grounded Video Question Answering (GVQA) task for the ICCV 2025 Perception Test Challenge. The GVQA task demands robus…
Object Discovery via Contrastive Learning for Weakly Supervised Object Detection
Jinhwan Seo, Wonho Bae, Danica J. Sutherland +2
Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using a model trained only on image-level annotations. Current state-of-the-art models benefit…
LID 2020: The Learning from Imperfect Data Challenge Results
Yunchao Wei, Shuai Zheng, Ming-Ming Cheng +32
Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotate…
Self-Augmentation: Generalizing Deep Networks to Unseen Classes for Few-Shot Learning
Jin-Woo Seo, Hong-Gyu Jung, Seong-Whan Lee
Few-shot learning aims to classify unseen classes with a few training examples. While recent works have shown that standard mini-batch training with a carefully designed training s…