3 citations · 6 across the 6 of their papers we have counts for
6 papers
Multimodal Self-Attention Network with Temporal Alignment for Audio-Visual Emotion Recognition
Inyong Koo, yeeun Seong, Minseok Son +2
Audio-visual emotion recognition (AVER) methods typically fuse utterance-level features, and even frame-level attention models seldom address the frame-rate mismatch across modalit…
Difficulty-aware Balancing Margin Loss for Long-tailed Recognition
Minseok Son, Inyong Koo, Jinyoung Park +1
When trained with severely imbalanced data, deep neural networks often struggle to accurately recognize classes with only a few samples. Previous studies in long-tailed recognition…
DiffRef3D: A Diffusion-based Proposal Refinement Framework for 3D Object Detection
Se-Ho Kim, Inyong Koo, Inyoung Lee +2
Denoising diffusion models show remarkable performances in generative tasks, and their potential applications in perception tasks are gaining interest. In this paper, we introduce…
PG-RCNN: Semantic Surface Point Generation for 3D Object Detection
Inyong Koo, Inyoung Lee, Se-Ho Kim +3
One of the main challenges in LiDAR-based 3D object detection is that the sensors often fail to capture the complete spatial information about the objects due to long distance and…
Explore-And-Match: Bridging Proposal-Based and Proposal-Free With Transformer for Sentence Grounding in Videos
Sangmin Woo, Jinyoung Park, Inyong Koo +3
Natural Language Video Grounding (NLVG) aims to localize time segments in an untrimmed video according to sentence queries. In this work, we present a new paradigm named Explore-An…
Improving Few-shot Learning with Weakly-supervised Object Localization
Inyong Koo, Minki Jeong, Changick Kim
Few-shot learning often involves metric learning-based classifiers, which predict the image label by comparing the distance between the extracted feature vector and class represent…