← Back to all news
arXiv Computer Vision By Motasem Alkayid, Yimin Zhu, Naser El-Sheimy, Lincoln Linlin Xu

Multi-task Disentangling Fusion Mamba-CNN for MODIS Spectral-Temporal Unmixing with Endmember Variability Modeling

Read the original on arXiv Computer Vision →

The paper introduces Multi-task Disentangling Fusion Mamba (MDF-Mamba), a framework for spectral-temporal unmixing of MODIS time series. It uses a disentangling architecture that pairs per-channel spectral experts with a bidirectional Mamba temporal branch, and integrates their outputs via a manifold-constrained hyper-connection (mHCFusion). The method also models endmember variability with pixel-wise learnable weights and employs a multi-task training objective combining supervised abundance estimation, self-supervised spectral reconstruction, and endmember regularization, achieving top performance on most evaluation metrics for Alberta and Saskatchewan datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Machine Learning
4d ago

Variational decomposition autoencoding improves disentanglement of latent representations

The paper introduces variational decomposition autoencoding (VDA), a framework that biases signal decomposition into latent representations. VDA is realized as variational decomposition autoencoders (DecVAEs), encoder-only networks that learn multiple latent subspaces aligned with time‑frequency characteristics. Experiments on simulated data and three speech datasets show that DecVAEs outperform traditional VAEs in disentanglement quality and robustness, producing factor‑specific latent subspaces when generative factors have distinct spectral signatures.

By Ioannis N. Ziogas, Aamna Al Shehhi, Ahsan H. Khandoker, Leontios J. Hadjileontiadis
multimodalsafety
More like this →
arXiv AI
21h ago

InfMasking: A Unified Variational Framework for Synergistic Multimodal Learning

InfMasking introduces a unified multimodal learning framework that uses Infinite Masking to encourage fusion modules to capture synergistic information across modalities. By randomly masking modality features before fusion and enforcing consistency with an unmasked reference, the method discourages reliance on dominant or shared signals and promotes meaningful cross‑modal interactions. The approach models masked fused representations as Gaussian, optimizing closed‑form variational bounds, and demonstrates improved predictive performance and higher synergy metrics on benchmark datasets.

By Liangjian Wen, Qun Dai, Jiang Duan, Yong Dai, Jianzhuang Liu, Dongkai Wang, Jun Wang, Fuyuan Cao, Zenglin Xu, Zhao Kang
multimodalbenchmarks
More like this →