6 papers
PDMP: Rethinking Balanced Multimodal Learning via Performance-Dominant Modality Prioritization
Shicai Wei, Chunbo Luo, Qiang Zhu +1
Multimodal learning has attracted increasing attention due to its practicality. However, it often suffers from insufficient optimization, where the multimodal model underperforms e…
One-stage Modality Distillation for Incomplete Multimodal Learning
Shicai Wei, Yang Luo, Chunbo Luo
Learning based on multimodal data has attracted increasing interest recently. While a variety of sensory modalities can be collected for training, not all of them are always availa…
Improving Multimodal Learning via Imbalanced Learning
Shicai Wei, Chunbo Luo, Yang Luo
Multimodal learning often encounters the under-optimized problem and may perform worse than unimodal learning. Existing approaches attribute this issue to imbalanced learning acros…
Boosting Multimodal Learning via Disentangled Gradient Learning
Shicai Wei, Chunbo Luo, Yang Luo
Multimodal learning often encounters the under-optimized problem and may have worse performance than unimodal learning. Existing methods attribute this problem to the imbalanced le…
OSDMamba: Enhancing Oil Spill Detection from Remote Sensing Images Using Selective State Space Model
Shuaiyu Chen, Fu Wang, Peng Ren +2
Semantic segmentation is commonly used for Oil Spill Detection (OSD) in remote sensing images. However, the limited availability of labelled oil spill samples and class imbalance p…
AE-DENet: Enhancement for Deep Learning-based Channel Estimation in OFDM Systems
Ephrem Fola, Yang Luo, Chunbo Luo
Deep learning (DL)-based methods have demonstrated remarkable achievements in addressing orthogonal frequency division multiplexing (OFDM) channel estimation challenges. However, e…