9 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…
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…
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…
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…
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…