Zobrazeno 1 - 10
of 10 180
pro vyhledávání: '"Yoon, Sung"'
Autor:
Im, Woobin, Cha, Geonho, Lee, Sebin, Lee, Jumin, Seon, Juhyeong, Wee, Dongyoon, Yoon, Sung-Eui
This paper presents a novel approach for reconstructing dynamic radiance fields from monocular videos. We integrate kinematics with dynamic radiance fields, bridging the gap between the sparse nature of monocular videos and the real-world physics. Ou
Externí odkaz:
http://arxiv.org/abs/2407.14059
Event cameras excel in capturing high-contrast scenes and dynamic objects, offering a significant advantage over traditional frame-based cameras. Despite active research into leveraging event cameras for semantic segmentation, generating pixel-wise d
Externí odkaz:
http://arxiv.org/abs/2407.11216
Existing open-set recognition (OSR) studies typically assume that each image contains only one class label, and the unknown test set (negative) has a disjoint label space from the known test set (positive), a scenario termed full-label shift. This pa
Externí odkaz:
http://arxiv.org/abs/2407.02386
Autor:
Yoon, Sung Min
Mean-reverting behavior of individuals assets is widely known in financial markets. In fact, we can construct a portfolio that has mean-reverting behavior and use it in trading strategies to extract profits. In this paper, we show that we are able to
Externí odkaz:
http://arxiv.org/abs/2406.17155
Generating reliable pseudo masks from image-level labels is challenging in the weakly supervised semantic segmentation (WSSS) task due to the lack of spatial information. Prevalent class activation map (CAM)-based solutions are challenged to discrimi
Externí odkaz:
http://arxiv.org/abs/2406.15755
This paper presents a novel method designed to enhance the efficiency and accuracy of both image retrieval and pixel retrieval. Traditional diffusion methods struggle to propagate spatial information effectively in conventional graphs due to their re
Externí odkaz:
http://arxiv.org/abs/2406.11242
Extending Segment Anything Model into Auditory and Temporal Dimensions for Audio-Visual Segmentation
Audio-visual segmentation (AVS) aims to segment sound sources in the video sequence, requiring a pixel-level understanding of audio-visual correspondence. As the Segment Anything Model (SAM) has strongly impacted extensive fields of dense prediction
Externí odkaz:
http://arxiv.org/abs/2406.06163
Trajectory prediction is a challenging problem that requires considering interactions among multiple actors and the surrounding environment. While data-driven approaches have been used to address this complex problem, they suffer from unreliable pred
Externí odkaz:
http://arxiv.org/abs/2403.10052
We present "SemCity," a 3D diffusion model for semantic scene generation in real-world outdoor environments. Most 3D diffusion models focus on generating a single object, synthetic indoor scenes, or synthetic outdoor scenes, while the generation of r
Externí odkaz:
http://arxiv.org/abs/2403.07773
Autor:
Lee, Jae-Jun, Yoon, Sung Whan
Meta-learning, which pursues an effective initialization model, has emerged as a promising approach to handling unseen tasks. However, a limitation remains to be evident when a meta-learner tries to encompass a wide range of task distribution, e.g.,
Externí odkaz:
http://arxiv.org/abs/2403.06768