Rethinking the Influence of Distribution Adjustment in Incremental Semantic Segmentation
Bibliographic record
Abstract
In an ever-changing world, incremental segmentation learning faces challenges due to the need for pixel-level accuracy and the practical application of gradually obtained samples. While most existing methods excel in stability by freezing model parameters or employing other regularization techniques to preserve the distribution of old knowledge, these approaches often fall short of achieving satisfactory plasticity. This phenomenon arises from the limited allocation of parameters for learning new knowledge. Meanwhile, in such a learning manner, the distribution of old knowledge cannot be optimized as new knowledge accumulates. As a result, the feature distribution of newly learned knowledge overlaps with old knowledge, leading to inaccurate segmentation performance on new classes and insufficient plasticity. This issue prompts us to explore how both old and new knowledge representations can be dynamically and simultaneously adjusted in the feature space during incremental learning. To address this, we conduct a theoretical structural analysis, which indicates that compressing the feature subspace and promoting sparse distribution is beneficial in allocating more space for new knowledge in incremental segmentation learning. Following compression principle, high-dimensional knowledge is projected into a lower-dimensional space in a contracted and dimensionally reduced manner. Regarding sparsity, the exclusivity of multiple peaks in Gaussian mixture distributions across different classes is preserved. Through effective knowledge transfer, both up-to-date and long-standing knowledge can dynamically adapt within a unified space, facilitating efficient adaptation to continuously incoming and evolving data. Extensive experiments across diverse incremental settings consistently validate the effectiveness of our proposed method. Source code is publicly available athttps://github.com/XinyueZhangHust/CSISL.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".