We lifted 10 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| chen-yang-liu/change-agent | canonical | 8 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| accuracy | Ran | chen-yang-liu/change-agent/Multi_change/utils_tool/utils.py code served (permissive licence) · get_code("53daad1d7b99fbdf") |
| build_vocab | Ran | chen-yang-liu/change-agent/Multi_change/preprocess_data.py code served (permissive licence) · get_code("922e2a3d44a7cf41") |
| cook_refs | Ran | chen-yang-liu/change-agent/Multi_change/eval_func/cider/cider_scorer.py code served (permissive licence) · get_code("4c1b107103b7c856") |
| cook_test | Ran | chen-yang-liu/change-agent/Multi_change/eval_func/cider/cider_scorer.py code served (permissive licence) · get_code("3573c4bc85f65dfd") |
| encode | Ran | chen-yang-liu/change-agent/Multi_change/preprocess_data.py code served (permissive licence) · get_code("9939e4093b93c961") |
| precook | Ran | chen-yang-liu/change-agent/Multi_change/eval_func/bleu/bleu_scorer.py code served (permissive licence) · get_code("ec4448373d72d636") |
| precook | Ran | chen-yang-liu/change-agent/Multi_change/eval_func/cider/cider_scorer.py code served (permissive licence) · get_code("9560e3e3e41bc1b2") |
| tokenize | Ran | chen-yang-liu/change-agent/Multi_change/preprocess_data.py code served (permissive licence) · get_code("c6e897c47140c3e2") |
| cook_refs | Not yet run | chen-yang-liu/change-agent/Multi_change/eval_func/bleu/bleu_scorer.py code served (permissive licence) · get_code("7586e1d32f7325f7") |
| cook_test | Not yet run | chen-yang-liu/change-agent/Multi_change/eval_func/bleu/bleu_scorer.py code served (permissive licence) · get_code("019ae8bc6f98133f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Remote sensing image classification forms the foundation of various understanding tasks, serving a crucial function in remote sensing image interpretation. The recent advancements of Convolutional Neural Networks (CNNs) and Transformers have markedly enhanced classification accuracy. Nonetheless, remote sensing scene classification remains a significant challenge, especially given the complexity and diversity of remote sensing scenarios and the variability of spatiotemporal resolutions. The capacity for whole-image understanding can provide more precise semantic cues for scene discrimination. In this paper, we introduce RSMamba, a novel architecture for remote sensing image classification. RSMamba is based on the State Space Model (SSM) and incorporates an efficient, hardware-aware design known as the Mamba. It integrates the advantages of both a global receptive field and linear modeling complexity. To overcome the limitation of the vanilla Mamba, which can only model causal sequences and is not adaptable to two-dimensional image data, we propose a dynamic multi-path activation mechanism to augment Mamba's capacity to model non-causal data. Notably, RSMamba maintains the inherent modeling mechanism of the vanilla Mamba, yet exhibits superior performance across multiple remote sensing image classification datasets. This indicates that RSMamba holds significant potential to function as the backbone of future visual foundation models. The code will be available at \url{https://github.com/KyanChen/RSMamba}.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2403.19654")
get_code_for_paper("2403.19654")
have("2403.19654")
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