32 citations · 67 across the 16 of their papers we have counts for
6 papers · 1 filter
Enhancing Modality Representation and Alignment for Multimodal Cold-start Active Learning
Meng Shen, Yake Wei, Jianxiong Yin +3
Training multimodal models requires a large amount of labeled data. Active learning (AL) aim to reduce labeling costs. Most AL methods employ warm-start approaches, which rely on s…
On-the-fly Modulation for Balanced Multimodal Learning
Yake Wei, Di Hu, Henghui Du +1
Multimodal learning is expected to boost model performance by integrating information from different modalities. However, its potential is not fully exploited because the widely-us…
Diagnosing and Re-learning for Balanced Multimodal Learning
Yake Wei, Siwei Li, Ruoxuan Feng +1
To overcome the imbalanced multimodal learning problem, where models prefer the training of specific modalities, existing methods propose to control the training of uni-modal encod…
MMPareto: Boosting Multimodal Learning with Innocent Unimodal Assistance
Yake Wei, Di Hu
Multimodal learning methods with targeted unimodal learning objectives have exhibited their superior efficacy in alleviating the imbalanced multimodal learning problem. However, in…
Multimodal Fusion on Low-quality Data: A Comprehensive Survey
Qingyang Zhang, Yake Wei, Zongbo Han +8
Multimodal fusion focuses on integrating information from multiple modalities with the goal of more accurate prediction, which has achieved remarkable progress in a wide range of s…
Quantifying and Enhancing Multi-modal Robustness with Modality Preference
Zequn Yang, Yake Wei, Ce Liang +1
Multi-modal models have shown a promising capability to effectively integrate information from various sources, yet meanwhile, they are found vulnerable to pervasive perturbations,…