Residual Adaptation

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Interpretable Hyperspectral Unmixing Framework with Fixed Endmember Prior and Structured Residual Refinement
Sep 8, 2026Ziyi Guan, Jianping Zhang, Qian LiuHyperspectral Unmixing Diffusion Refinement
CEDAR: Error-Bounded Residual Routing for Efficient Long-Context Attention
Sep 7, 2026Siyu Li, Dong Wang, Jie Zhou +3Dynamic Sparse Attention Efficient Long-Context Inference
Vid2WAM: Distilling Video Diffusion Priors into World Action Models
Aug 9, 2026Chenhao Qiu, Ruixiang Wang, Runyi Zhao +7Efficient World-Action Model Video Pretraining
LoRSA: Toward Generalizable Parameter-Efficient Fine-Tuning for Biomedical Downstream Tasks
Aug 7, 2026Saed Moradi, Benyamin Ghojogh, M. Hadi Sepanj +2Low-Rank Adaptation Parameter-Efficient Fine-Tuning
Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces
Jul 20, 2026Dongming Wang, Pengcheng Dai, Wenwu Yu +1Multi-Agent Reinforcement Learning Markov Decision Processes
DARA: Degradation-Aware Low-Rank Residual Adaptation with Original-to-Corrupted Distillation for Corruption-Robust Animal Re-Identification
Jul 18, 2026Cynthia Xie, Talia XuAnimal Re-Identification Re-Identification
GaitSpan: Growing Humanoid Locomotion from Walking to Running
Jul 13, 2026Kwan-Yee Lin, Zilin Wang, Janelle J. Liu +1Perceptive Locomotion Policies Locomotion Stability
FADRA: Frequency-Aware Diffusion with Residual Adaptation for Video Face Restoration
Jul 7, 2026Jin Jiang, Jia Wang, Panwen Hu +2Photorealistic Video Restoration Blind Face Restoration
DLR: Zero-Inference-Cost Latent Residuals for Low-Rank Pre-Training
Jun 27, 2026Dong Wang, Wenwu Tang, Yun Cheng +1Large Language Model Pretraining Low-Rank Factorization
Learning the Context of Errors: Black-Box Online Adaptation of Time Series Foundation Models
Jun 12, 2026Xilin Dai, Yiding Liu, Hongjie Xia +4Time Series Foundation Models Zero-Shot Forecasting
DRIFT: A Residual Flow Adapter for Decoding Continuous Outputs in Vision-Language Models
Jun 4, 2026Zhuoming Liu, Jinhong Lin, Kwan Man Cheng +3Residual Adaptation Autoregressive Decoding
Residual Decoder Adapter: ID-Preserving Tokenizer Adaption for Autoregressive Text Rendering
Jun 1, 2026Dongxing Mao, Jinpeng Wang, Jiahao Tang +6Autoregressive Image Generation Visual Autoregressive Models
CRMA: A Spectrally-Bounded Backbone for Modular Continual Fine-Tuning of LLMs
May 29, 2026Kiran Nayudu, Aswini Nutakki, Sai Vinay Naidu +1Parameter-Efficient Fine-Tuning Residual Adaptation
BORA: Bridging Offline Reinforcement Learning and Online Residual Adaptation for Real-World Dexterous VLA Models
May 28, 2026Zhongxi Chen, Yifan Han, Yanming Shao +5Imitation Learning Offline Reinforcement Learning
A Geometric View of SRC: Learning Representations for Stable Residual Inference
May 28, 2026Vangelis P. OikonomouResidual Learning Bias Learning
When Should an AI Scientist Stop? Verifiable Experiment Steering and Refusal for Autonomous Discovery
May 26, 2026Neel Tushar Shah, Manglam KartikArtificial Intelligence Scientists Verifier
Restoration-Aligned Generative Flow Models for Blind Motion Deblurring
May 9, 2026Insoo Kim, Jinwoo ShinMotion Blur Generative Flow Networks
Evidence Recomposition and Predictive Context Residualization for Visual Attribution in Multimodal Large Language Models
Sep 26, 2025Jiawei Liang, Jianjie Huang, Ruoyu Chen +4Visual Attribution Residual Adaptation

Sep 8, 2026 · cs.CVJ/K move · Enter open · S save

Interpretable Hyperspectral Unmixing Framework with Fixed Endmember Prior and Structured Residual Refinement

Ziyi Guan, Jianping Zhang, Qian Liu

School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China

Hyperspectral unmixing decomposes mixed pixels into material endmembers and their abundances from contiguous spectral observations. In modular sensing pipelines, endmembers are often first identified and then treated as fixed during abundance estimation. When this fixed endmember prior is inaccurate, spatially structured mismatch arising from illumination changes, sensor artifacts, or material boundaries may be incorrectly captured by the abundance variables, leading to unstable decompositions. This study presents an interpretable stage-wise hyperspectral unmixing framework (I-HyperSU) under fixed endmember priors, which is explicitly decomposed into a fixed endmember matrix

\mathbf{A}

, an abundance block

\mathbf{X}

, and a structural residual refinement block

\mathbf{S}

. The X-block estimates abundances using FISTA with nonnegativity and sparsity enhancement, and a soft penalty that approximately enforces sum-to-one constraints. The S-block jointly applies low-rank SVD structural regularization and a lightweight deep image prior (DIP) to refine structured residuals. This staged design makes the interaction between abundance and residual components transparent and interpretable. Experiments on Samson, Urban, and Jasper Ridge datasets demonstrate that, under fixed and imperfect endmember priors, soft abundance relaxation consistently outperforms hard simplex projection. Under the default N-FINDR endmember prior, the proposed framework reduces the joint reconstruction error by 61.7%--69.5% compared with a fixed-

\mathbf{A}

UCLS baseline, while keeping the abundance RMSE nearly unchanged, indicating that the residual refinement branch accounts for structured model mismatch without degrading the abundance estimates. For example, on Urban, the reconstruction SAM decreases from

5.99^\circ

for the X-only model to

1.92^\circ

for the full model.

Hyperspectral Unmixing Diffusion Refinement Deep Unfolding Networks Residual Adaptation Singular Value Decomposition Mismatch Finite

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