Grunnleggende konsepter
CoMasTRe proposes a two-stage segmenter for continual segmentation by disentangling objectness learning and class recognition, achieving superior performance compared to existing methods.
Sammendrag
Continual segmentation is addressed by CoMasTRe, which disentangles objectness learning and class recognition. The method outperforms per-pixel baselines and query-based methods on datasets like PASCAL VOC and ADE20K. By distilling objectness and classification knowledge separately, CoMasTRe achieves a balance between stability and plasticity in learning new classes while preserving old class knowledge.
Most continual segmentation methods tackle the problem as a per-pixel classification task. However, CoMasTRe proposes a different approach by disentangling continual segmentation into forgetting-resistant continual objectness learning and well-researched continual classification. The method uses distillation strategies to reinforce objectness and alleviate forgetting of old classes during long learning processes.
The success of neural nets in computer vision tasks has been limited to single-shot learning scenarios. Continual learning aims to gradually obtain knowledge in a sequential fashion, mimicking human learning processes. Dense prediction tasks like semantic segmentation are particularly challenging due to the laborious annotation process and inaccessible learned samples in scenarios like autonomous driving or medical imaging.
CoMasTRe leverages the properties of objectness to simplify continual segmentation by decoupling the task into objectness learning and class recognition stages. By distilling objectness scores, mask proposals, and positional embeddings separately, the method effectively mitigates forgetting issues while improving overall performance on both base and incremented classes.
Could we mitigate forgetting in continual segmentation more effectively? The answer lies in a new paradigm: mask classification streamlining continual segmentation as learning without forgetting of class-agnostic binary masks and class recognition via query-based segmenters.
Objectness in query-based methods helps generalize mask proposals on unseen classes similar to learned classes. Additionally, because of the transfer ability of objectness, query-based methods are resistant to catastrophic forgetting of mask proposals.
To sum up, CoMasTRe's contributions include leveraging objectness properties for continual segmentation, proposing a two-stage segmenter for disentangled learning, reinforcing old class knowledge through distillation strategies, achieving state-of-the-art results on benchmark datasets like PASCAL VOC 2012 and ADE20K.
Statistikk
Based on our findings...
Extensive experiments show that...
Compared with previous best methods...
The results indicate...
Sitater
"Most continual segmentation methods tackle the problem as a per-pixel classification task."
"CoMasTRe proposes a different approach by disentangling continual segmentation into forgetting-resistant continual objectness learning."
"The method outperforms per-pixel baselines and query-based methods on datasets like PASCAL VOC."