32 citations · 69 across the 6 of their papers we have counts for
12 papers
Are Multimodal Transformers Robust to Missing Modality?
Mengmeng Ma, Jian Ren, Long Zhao +2
Multimodal data collected from the real world are often imperfect due to missing modalities. Therefore multimodal models that are robust against modal-incomplete data are highly pr…
Global Matching with Overlapping Attention for Optical Flow Estimation
Shiyu Zhao, Long Zhao, Zhixing Zhang +2
Optical flow estimation is a fundamental task in computer vision. Recent direct-regression methods using deep neural networks achieve remarkable performance improvement. However, t…
SMIL: Multimodal Learning with Severely Missing Modality
Mengmeng Ma, Jian Ren, Long Zhao +3
A common assumption in multimodal learning is the completeness of training data, i.e., full modalities are available in all training examples. Although there exists research endeav…
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization
Long Zhao, Yuxiao Wang, Jiaping Zhao +7
We introduce a novel representation learning method to disentangle pose-dependent as well as view-dependent factors from 2D human poses. The method trains a network using cross-vie…
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness
Long Zhao, Ting Liu, Xi Peng +1
Adversarial data augmentation has shown promise for training robust deep neural networks against unforeseen data shifts or corruptions. However, it is difficult to define heuristic…
Knowledge as Priors: Cross-Modal Knowledge Generalization for Datasets without Superior Knowledge
Long Zhao, Xi Peng, Yuxiao Chen +2
Cross-modal knowledge distillation deals with transferring knowledge from a model trained with superior modalities (Teacher) to another model trained with weak modalities (Student)…