81 citations · 113 across the 13 of their papers we have counts for
19 papers
POPS: Recovering Unlearned Multi-Modality Knowledge in MLLMs with Prompt-Optimized Parameter Shaking
Zhangheng LI, Jianing Zhu, Junyuan Hong +4
Multimodal Large Language Models (MLLMs) have demonstrated impressive performance on cross-modal tasks by jointly training on large-scale textual and visual data, where privacy-sen…
Generative Spatiotemporal Data Augmentation
Jinfan Zhou, Lixin Luo, Sungmin Eum +2
We explore spatiotemporal data augmentation using video foundation models to diversify both camera viewpoints and scene dynamics. Unlike existing approaches based on simple geometr…
Coarse-to-Fine Hierarchical Alignment for UAV-based Human Detection using Diffusion Models
Wenda Li, Meng Wu, Liangzhao Chen +3
Training object detectors demands extensive, task-specific annotations, yet this requirement becomes impractical in UAV-based human detection due to constantly shifting target dist…
MoRe: Monocular Geometry Refinement via Graph Optimization for Cross-View Consistency
Dongki Jung, Jaehoon Choi, Yonghan Lee +3
Monocular 3D foundation models offer an extensible solution for perception tasks, making them attractive for broader 3D vision applications. In this paper, we propose MoRe, a train…
UAV4D: Dynamic Neural Rendering of Human-Centric UAV Imagery using Gaussian Splatting
Jaehoon Choi, Dongki Jung, Christopher Maxey +4
Despite significant advancements in dynamic neural rendering, existing methods fail to address the unique challenges posed by UAV-captured scenarios, particularly those involving m…
UAVTwin: Neural Digital Twins for UAVs using Gaussian Splatting
Jaehoon Choi, Dongki Jung, Yonghan Lee +3
We present UAVTwin, a method for creating digital twins from real-world environments and facilitating data augmentation for training downstream models embedded in unmanned aerial v…