20 citations · 29 across the 12 of their papers we have counts for
12 papers
Balancing Multimodal Learning through Label Space Reshaping
Xiaoyu Ma, Weijie Zhang, Yuanhao Gao +3
Multimodal learning often suffers from modality imbalance, where modalities that converge faster dominate optimization while others remain undertrained. Existing approaches typical…
Improving Multimodal Learning Balance and Sufficiency through Data Remixing
Xiaoyu Ma, Hao Chen, Yongjian Deng
Different modalities hold considerable gaps in optimization trajectories, including speeds and paths, which lead to modality laziness and modality clash when jointly training multi…
Spatially-guided Temporal Aggregation for Robust Event-RGB Optical Flow Estimation
Qianang Zhou, Junhui Hou, Meiyi Yang +3
Current optical flow methods exploit the stable appearance of frame (or RGB) data to establish robust correspondences across time. Event cameras, on the other hand, provide high-te…
ResFlow: Fine-tuning Residual Optical Flow for Event-based High Temporal Resolution Motion Estimation
Qianang Zhou, Zhiyu Zhu, Junhui Hou +3
Event cameras hold significant promise for high-temporal-resolution (HTR) motion estimation. However, estimating event-based HTR optical flow faces two key challenges: the absence…
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…
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…
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…
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…
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…
Event Voxel Set Transformer for Spatiotemporal Representation Learning on Event Streams
Bochen Xie, Yongjian Deng, Zhanpeng Shao +2
Event cameras are neuromorphic vision sensors that record a scene as sparse and asynchronous event streams. Most event-based methods project events into dense frames and process th…
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…
A Voxel Graph CNN for Object Classification with Event Cameras
Yongjian Deng, Hao Chen, Hai Liu +1
Event cameras attract researchers' attention due to their low power consumption, high dynamic range, and extremely high temporal resolution. Learning models on event-based object c…