2 citations · 5 across the 13 of their papers we have counts for
14 papers
EviSplat: Preserving Multi-View Evidence in 3D Gaussian Splatting for Open-Vocabulary Segmentation
Sungho Moon, Kota Shimomura, Junwoo Park +4
Open-vocabulary 3D scene understanding enables object localization and segmentation from free-form text queries without a fixed category vocabulary. Many recent methods build on 3D…
CascadeOcc: Rethinking 3D Occupancy World Models with Cascaded VQ Representations
Kyumin Hwang, Wonhyeok Choi, Jaeyeul Kim +3
This letter proposes CascadeOcc, a novel occupancy world model that prioritizes intrinsic structural hierarchy over extrinsic auxiliary modalities for autonomous driving. Occupancy…
A Review of Online Diffusion Policy RL Algorithms for Scalable Robotic Control
Wonhyeok Choi, Shutong Ding, Minwoo Choi +5
Diffusion policies have emerged as a powerful approach for robotic control, demonstrating superior expressiveness in modeling multimodal action distributions compared to convention…
Scale-invariant and View-relational Representation Learning for Full Surround Monocular Depth
Kyumin Hwang, Wonhyeok Choi, Kiljoon Han +5
Recent foundation models demonstrate strong generalization capabilities in monocular depth estimation. However, directly applying these models to Full Surround Monocular Depth Esti…
Infinite-Story: A Training-Free Consistent Text-to-Image Generation
Jihun Park, Kyoungmin Lee, Jongmin Gim +7
We present Infinite-Story, a training-free framework for consistent text-to-image (T2I) generation tailored for multi-prompt storytelling scenarios. Built upon a scale-wise autoreg…
Prompt-Based Safety Guidance Is Ineffective for Unlearned Text-to-Image Diffusion Models
Jiwoo Shin, Byeonghu Na, Mina Kang +2
Recent advances in text-to-image generative models have raised concerns about their potential to produce harmful content when provided with malicious input text prompts. To address…
Temporal Grounding as a Learning Signal for Referring Video Object Segmentation
Seunghun Lee, Jiwan Seo, Jeonghoon Kim +9
Referring Video Object Segmentation (RVOS) aims to segment and track objects in videos based on natural language expressions, requiring precise alignment between visual content and…
A Training-Free Style-Personalization via SVD-Based Feature Decomposition
Kyoungmin Lee, Jihun Park, Jongmin Gim +4
We present a training-free framework for style-personalized image generation that operates during inference using a scale-wise autoregressive model. Our method generates a stylized…
Intrinsic Image Decomposition for Robust Self-supervised Monocular Depth Estimation on Reflective Surfaces
Wonhyeok Choi, Kyumin Hwang, Minwoo Choi +4
Self-supervised monocular depth estimation (SSMDE) has gained attention in the field of deep learning as it estimates depth without requiring ground truth depth maps. This approach…
Self-supervised Monocular Depth Estimation Robust to Reflective Surface Leveraged by Triplet Mining
Wonhyeok Choi, Kyumin Hwang, Wei Peng +2
Self-supervised monocular depth estimation (SSMDE) aims to predict the dense depth map of a monocular image, by learning depth from RGB image sequences, eliminating the need for gr…
Multi-task Learning for Real-time Autonomous Driving Leveraging Task-adaptive Attention Generator
Wonhyeok Choi, Mingyu Shin, Hyukzae Lee +3
Real-time processing is crucial in autonomous driving systems due to the imperative of instantaneous decision-making and rapid response. In real-world scenarios, autonomous vehicle…
Depth-discriminative Metric Learning for Monocular 3D Object Detection
Wonhyeok Choi, Mingyu Shin, Sunghoon Im
Monocular 3D object detection poses a significant challenge due to the lack of depth information in RGB images. Many existing methods strive to enhance the object depth estimation…
Dynamic Neural Network for Multi-Task Learning Searching across Diverse Network Topologies
Wonhyeok Choi, Sunghoon Im
In this paper, we present a new MTL framework that searches for structures optimized for multiple tasks with diverse graph topologies and shares features among tasks. We design a r…
ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation
Seunghun Lee, Wonhyeok Choi, Changjae Kim +2
In this paper, we present a direct adaptation strategy (ADAS), which aims to directly adapt a single model to multiple target domains in a semantic segmentation task without pretra…