9 citations · 12 across the 7 of their papers we have counts for
6 papers · 1 filter
Scaling Dense Event-Stream Pretraining from Visual Foundation Models
Zhiwen Chen, Junhui Hou, Zhiyu Zhu +2
Learning versatile, fine-grained representations from irregular event streams is pivotal yet nontrivial, primarily due to the heavy annotation that hinders scalability in dataset s…
Optimizing Multi-Modality Trackers via Significance-Regularized Tuning
Zhiwen Chen, Jinjian Wu, Zhiyu Zhu +3
This paper tackles the critical challenge of optimizing multi-modality trackers by effectively adapting pre-trained models for RGB data. Existing fine-tuning paradigms oscillate be…
E-Motion: Future Motion Simulation via Event Sequence Diffusion
Song Wu, Zhiyu Zhu, Junhui Hou +2
Forecasting a typical object's future motion is a critical task for interpreting and interacting with dynamic environments in computer vision. Event-based sensors, which could capt…
Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement
Huachen Fang, Jinjian Wu, Qibin Hou +2
Previous deep learning-based event denoising methods mostly suffer from poor interpretability and difficulty in real-time processing due to their complex architecture designs. In t…
Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration
Yifan Zhang, Junhui Hou, Siyu Ren +3
This paper introduces a novel self-supervised learning framework for enhancing 3D perception in autonomous driving scenes. Specifically, our approach, namely NCLR, focuses on 2D-3D…
Segment Any Events via Weighted Adaptation of Pivotal Tokens
Zhiwen Chen, Zhiyu Zhu, Yifan Zhang +3
In this paper, we delve into the nuanced challenge of tailoring the Segment Anything Models (SAMs) for integration with event data, with the overarching objective of attaining robu…