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20232025
most citedA Dynamic Graph CNN with Cross-Representation Distillation for Event-Based Recognition

3 citations · 5 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.CV2024★ 2 cited

Prune and Repaint: Content-Aware Image Retargeting for any Ratio

Feihong Shen, Chao Li, Yifeng Geng +2

Image retargeting is the task of adjusting the aspect ratio of images to suit different display devices or presentation environments. However, existing retargeting methods often st…

cs.CV2024

EventAug: Multifaceted Spatio-Temporal Data Augmentation Methods for Event-based Learning

Yukun Tian, Hao Chen, Yongjian Deng +4

The event camera has demonstrated significant success across a wide range of areas due to its low time latency and high dynamic range. However, the community faces challenges such…

cs.CV2024

Event-based Video Frame Interpolation with Edge Guided Motion Refinement

Yuhan Liu, Yongjian Deng, Hao Chen +3

Video frame interpolation, the process of synthesizing intermediate frames between sequential video frames, has made remarkable progress with the use of event cameras. These sensor…

cs.CV2024

Data-efficient Event Camera Pre-training via Disentangled Masked Modeling

Zhenpeng Huang, Chao Li, Hao Chen +3

In this paper, we present a new data-efficient voxel-based self-supervised learning method for event cameras. Our pre-training overcomes the limitations of previous methods, which…

cs.CV2023

Dissecting RGB-D Learning for Improved Multi-modal Fusion

Hao Chen, Haoran Zhou, Yunshu Zhang +2

In the RGB-D vision community, extensive research has been focused on designing multi-modal learning strategies and fusion structures. However, the complementary and fusion mechani…

cs.CV2023★ 3 cited

A Dynamic Graph CNN with Cross-Representation Distillation for Event-Based Recognition

Yongjian Deng, Hao Chen, Bochen Xie +2

Recent advances in event-based research prioritize sparsity and temporal precision. Approaches using dense frame-based representations processed via well-pretrained CNNs are being…