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.
By Motasem Alkayid, Yimin Zhu, Naser El-Sheimy, Lincoln Linlin Xu
arXiv:2608. 08207v1 Announce Type: cross Abstract: Multivariate Time Series Classification (MTSC) demands models that can effectively capture complex temporal patterns across multiple scales while remaining computationally efficient.
By Pingping Liu, Muyao Wang, Zijian Zhang, Tongshun Zhang, Hao Miao, Guorui Xie, Qingliang Li, Qiuzhan Zhou
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
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
ChorusTIC is a training‑free foundation model for multivariate time‑series classification that works across heterogeneous channel configurations without updating task‑specific parameters. It uses Random Subchannel Slot Concatenation and a shared dual‑axis encoder to capture temporal and cross‑channel interactions, mapping variable channel counts into a fixed‑width representation. The model is pretrained on synthetic episodes and achieves strong performance on the UEA‑30 and UCR‑128 archives, handling both full‑context and low‑label scenarios without fitting a target‑specific classifier.
By Juntao Fang, Shifeng Xie, Ruichu Cai, Shengji Zheng, Zijian Li, Keli Zhang, Lujia Pan, Themis Palpanas, Zhifeng Hao
Multispectral aerial object detection aims to exploit spectral-spatial cues to address object detection challenges in complex aerial scenes. Existing methods mainly focus on spectral-spatial decouplin...
The paper introduces MIDAPN, a unified spatiotemporal forecasting backbone that integrates media-general graph adaptation and automatic temporal learning for both multivariate and multimodal time series. It employs a Multimedia Identity-Aware Graph to capture static, dynamic, and latent relationships across media, and a Spectral Prism Convolution for hierarchical temporal analysis. Extensive experiments on 13 multivariate and 12 multimodal datasets show MIDAPN consistently outperforms 16 state‑of‑the‑art TSF models and is compatible with various foundation models.
By Jierui Lei, Wenjian Zhang, Qingyi Yang, Yuyang Hong, Fangzheng Chen, Zhengbo Zhang, Haina Tang, Shiming Xiang
The paper introduces S$^3$F-Net, a dual‑branch network that fuses spatial and spectral representations for medical image classification. It combines a deep spatial CNN with a shallow spectral encoder, SpectraNet, which uses a learnable SpectralFilter layer to process the full Fourier spectrum efficiently. Evaluated on four medical imaging datasets, S$^3$F-Net consistently outperforms spatial‑only baselines, achieving state‑of‑the‑art accuracy on BRISC2025 and surpassing deeper models on the Chest X‑Ray Pneumonia dataset.
By Md. Saiful Bari Siddiqui, Mohammed Imamul Hassan Bhuiyan
arXiv:2610.27883v1 Announce Type: new
Abstract: Multispectral aerial object detection aims to exploit spectral-spatial cues to address object detection challenges in complex aerial scenes. Existing m...
By Peng Zhang, Shuaihao Han, Tingfa Xu, Jianan Li
arXiv:2606.08906v2 Announce Type: replace
Abstract: In many binary segmentation tasks, most multimodal methods rely on fixed feature concatenation for cross-modal interaction and straightforward deco...
By Qiangqiang Zhou, Jiawei Xu, Yong Chen, Dandan Zhu, Yugen Yi, Xiaoqi Zhao
arXiv:2608. 02769v1 Announce Type: cross Abstract: Multimodal supervised learning seeks to leverage multiple heterogeneous data sources to improve predictive performance.
By Sagnik Nandy, Samriddha Lahiry, Pragya Sur, Subhabrata Sen
LESSViT introduces a sensor‑flexible Vision Transformer for hyperspectral imagery that uses a low‑rank structured attention mechanism (LESS Attention) to efficiently model joint spatial‑spectral interactions. By reducing the complexity from ≠O(N^2C^2) to ≠O(rNC) and adding channel‑agnostic patch embedding with wavelength‑aware positional encoding, it supports flexible spectral inputs. A hyperspectral masked autoencoder (HyperMAE) with decoupled spatial‑spectral masking further enables robust pretraining, and experiments on the SpectralEarth benchmark show improved robustness to spectral shifts while maintaining competitive in‑distribution performance.
By Haozhe Si, Yuxuan Wan, Yuqing Wang, Minh N. Do, Han Zhao