Continual Segmentation with Objectness Learning and Class Recognition

Core Concepts

CoMasTRe proposes a two-stage segmenter for continual segmentation by disentangling objectness learning and class recognition, achieving superior performance compared to existing methods.

Abstract

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.

Stats

Based on our findings...
Extensive experiments show that...
Compared with previous best methods...
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Quotes

"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."

Key Insights Distilled From

Continual Segmentation with Disentangled Objectness Learning and Class Recognition

by Yizheng Gong... at arxiv.org 03-07-2024

https://arxiv.org/pdf/2403.03477.pdf

Deeper Inquiries

How can CoMasTRe's approach be extended beyond semantic segmentation?

CoMasTRe's approach of disentangling objectness learning from class recognition can be extended to various other tasks in computer vision and beyond. For instance, in object detection, the separation of detecting objects (objectness) from classifying them could lead to more efficient models. This methodology could also be applied to natural language processing tasks like text classification or sentiment analysis, where distinguishing between identifying textual patterns and assigning labels could enhance model performance. Furthermore, in reinforcement learning, separating action selection (objectness) from policy evaluation (class recognition) could improve continual learning capabilities.

What counterarguments exist against the effectiveness of disentangling objectness from class recognition?

One potential counterargument against disentangling objectness from class recognition is the risk of losing holistic understanding during inference. By separating these components, there might be a loss of contextual information that arises when considering both aspects together. Additionally, this separation may introduce additional complexity into the model architecture and training process, potentially leading to increased computational costs and longer training times. Moreover, if not implemented carefully, decoupling objectness learning from class recognition may result in suboptimal performance due to challenges in aligning the learned representations effectively.

How does CoMasTRe's methodology relate to broader concepts in machine learning beyond computer vision?

CoMasTRe's methodology aligns with broader concepts such as modularization and transfer learning within machine learning. By breaking down the segmentation task into distinct stages for objectness learning and class recognition, it follows a modular approach that enhances interpretability and flexibility within the model architecture. Additionally, leveraging pretraining on related tasks like COCO dataset demonstrates transfer learning principles by utilizing knowledge gained from one domain to improve performance on another domain (PASCAL VOC). The distillation techniques employed by CoMasTRe resonate with lifelong/continual learning paradigms by focusing on retaining past knowledge while acquiring new information over time—a fundamental aspect across various machine-learning applications seeking adaptability and scalability.