Beyond Quadratic: Linear-Time Change Detection with RWKV
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912975438217216 |
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| author | Yang, Zhenyu Pei, Gensheng Chen, Tao Yuan, Xia Zhang, Haofeng Shu, Xiangbo Yao, Yazhou |
| author_facet | Yang, Zhenyu Pei, Gensheng Chen, Tao Yuan, Xia Zhang, Haofeng Shu, Xiangbo Yao, Yazhou |
| contents | Existing paradigms for remote sensing change detection are caught in a trade-off: CNNs excel at efficiency but lack global context, while Transformers capture long-range dependencies at a prohibitive computational cost. This paper introduces ChangeRWKV, a new architecture that reconciles this conflict. By building upon the Receptance Weighted Key Value (RWKV) framework, our ChangeRWKV uniquely combines the parallelizable training of Transformers with the linear-time inference of RNNs. Our approach core features two key innovations: a hierarchical RWKV encoder that builds multi-resolution feature representation, and a novel Spatial-Temporal Fusion Module (STFM) engineered to resolve spatial misalignments across scales while distilling fine-grained temporal discrepancies. ChangeRWKV not only achieves state-of-the-art performance on the LEVIR-CD benchmark, with an 85.46% IoU and 92.16% F1 score, but does so while drastically reducing parameters and FLOPs compared to previous leading methods. This work demonstrates a new, efficient, and powerful paradigm for operational-scale change detection. Our code and model are publicly available. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_19606 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Quadratic: Linear-Time Change Detection with RWKV Yang, Zhenyu Pei, Gensheng Chen, Tao Yuan, Xia Zhang, Haofeng Shu, Xiangbo Yao, Yazhou Computer Vision and Pattern Recognition Existing paradigms for remote sensing change detection are caught in a trade-off: CNNs excel at efficiency but lack global context, while Transformers capture long-range dependencies at a prohibitive computational cost. This paper introduces ChangeRWKV, a new architecture that reconciles this conflict. By building upon the Receptance Weighted Key Value (RWKV) framework, our ChangeRWKV uniquely combines the parallelizable training of Transformers with the linear-time inference of RNNs. Our approach core features two key innovations: a hierarchical RWKV encoder that builds multi-resolution feature representation, and a novel Spatial-Temporal Fusion Module (STFM) engineered to resolve spatial misalignments across scales while distilling fine-grained temporal discrepancies. ChangeRWKV not only achieves state-of-the-art performance on the LEVIR-CD benchmark, with an 85.46% IoU and 92.16% F1 score, but does so while drastically reducing parameters and FLOPs compared to previous leading methods. This work demonstrates a new, efficient, and powerful paradigm for operational-scale change detection. Our code and model are publicly available. |
| title | Beyond Quadratic: Linear-Time Change Detection with RWKV |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.19606 |