GLFRNet: Global-Local Feature Refusion Network for Remote Sensing Image Instance Segmentation
Bibliographic record
Abstract
Instance segmentation is a significant way for remote sensing image (RSI) interpretation. The large number, sharp variation of sizes, and complex background of objects raise higher demands for instance segmentation models. The synergistic usage of global and local features has drawn great attention due to its superior performance but has not been fully explored in mainstream instance segmentation methods. In this work, a global-local feature refusion network (GLFRNet) with two fusion procedures is proposed to fully utilize coarse-grained and fine-grained features for RSI instance segmentation. In this model, the backbone integrates both convolutional neural network (CNN)-based and VMamba-based branches to extract local and global features, respectively. Three novel models are proposed to leverage the features adaptively, i.e., the cross-dim feature fusion (CDFF) module, the semantic complementary feature fusion (SCFF) module, and the guided feature refusion module (GFRM). The CDFF module is designed to aggregate features flexibly by fusing features from two backbones with different attention modules in the first fusion procedure. The GFRM and SCFF module are proposed in the refusion procedure to generate accurate segmentation results. Inspired by agent attention, the GFRM dynamically assembles detailed features for mask generation by refusing local and global features with the guidance of fusion results from CDFF. The SCFF module complements the significant features by enhancing and integrating global, local, and detailed features, and finally generates masks of instances. Extensive experiments demonstrate that GLFRNet outperforms the second-best model by 1.9, 1.3, and 0.3 in mask average precisions (APs) on NWPU VHR-10, WHU Building, and iSAID datasets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".