Balanced Residual Distillation Learning for 3D Point Cloud Class-Incremental Semantic Segmentation

• URL: http://arxiv.org/abs/2408.01356v1
• Date: Fri, 2 Aug 2024 16:09:06 GMT
• Title: Balanced Residual Distillation Learning for 3D Point Cloud Class-Incremental Semantic Segmentation
• Authors: Yuanzhi Su, Siyuan Chen, Yuan-Gen Wang,
• Abstract summary: Class-incremental learning (CIL) thrives due to its success in processing the influx of information by learning from continuously added new classes. We propose a novel balanced residual distillation framework (BRD-CIL) to push the performance bar of CIL to a new higher level.
• Score: 13.627816749091727
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Class-incremental learning (CIL) thrives due to its success in processing the influx of information by learning from continuously added new classes while preventing catastrophic forgetting about the old ones. It is essential for the performance breakthrough of CIL to effectively refine past knowledge from the base model and balance it with new learning. However, such an issue has not yet been considered in current research. In this work, we explore the potential of CIL from these perspectives and propose a novel balanced residual distillation framework (BRD-CIL) to push the performance bar of CIL to a new higher level. Specifically, BRD-CIL designs a residual distillation learning strategy, which can dynamically expand the network structure to capture the residuals between the base and target models, effectively refining the past knowledge. Furthermore, BRD-CIL designs a balanced pseudo-label learning strategy by generating a guidance mask to reduce the preference for old classes, ensuring balanced learning from new and old classes. We apply the proposed BRD-CIL to a challenging 3D point cloud semantic segmentation task where the data are unordered and unstructured. Extensive experimental results demonstrate that BRD-CIL sets a new benchmark with an outstanding balance capability in class-biased scenarios.

Related papers

• Strike a Balance in Continual Panoptic Segmentation [60.26892488010291]
  We introduce past-class backtrace distillation to balance the stability of existing knowledge with the adaptability to new information. We also introduce a class-proportional memory strategy, which aligns the class distribution in the replay sample set with that of the historical training data. We present a new method named Continual Panoptic Balanced (BalConpas)
  arXiv  Detail & Related papers  (2024-07-23T09:58:20Z)
• ACTRESS: Active Retraining for Semi-supervised Visual Grounding [52.08834188447851]
  A previous study, RefTeacher, makes the first attempt to tackle this task by adopting the teacher-student framework to provide pseudo confidence supervision and attention-based supervision. This approach is incompatible with current state-of-the-art visual grounding models, which follow the Transformer-based pipeline. Our paper proposes the ACTive REtraining approach for Semi-Supervised Visual Grounding, abbreviated as ACTRESS.
  arXiv  Detail & Related papers  (2024-07-03T16:33:31Z)
• Improving Data-aware and Parameter-aware Robustness for Continual Learning [3.480626767752489]
  This paper analyzes that this insufficiency arises from the ineffective handling of outliers. We propose a Robust Continual Learning (RCL) method to address this issue. The proposed method effectively maintains robustness and achieves new state-of-the-art (SOTA) results.
  arXiv  Detail & Related papers  (2024-05-27T11:21:26Z)
• Dynamic Sub-graph Distillation for Robust Semi-supervised Continual Learning [52.046037471678005]
  We focus on semi-supervised continual learning (SSCL), where the model progressively learns from partially labeled data with unknown categories. We propose a novel approach called Dynamic Sub-Graph Distillation (DSGD) for semi-supervised continual learning.
  arXiv  Detail & Related papers  (2023-12-27T04:40:12Z)
• Mitigating Forgetting in Online Continual Learning via Contrasting Semantically Distinct Augmentations [22.289830907729705]
  Online continual learning (OCL) aims to enable model learning from a non-stationary data stream to continuously acquire new knowledge as well as retain the learnt one. Main challenge comes from the "catastrophic forgetting" issue -- the inability to well remember the learnt knowledge while learning the new ones.
  arXiv  Detail & Related papers  (2022-11-10T05:29:43Z)
• FOSTER: Feature Boosting and Compression for Class-Incremental Learning [52.603520403933985]
  Deep neural networks suffer from catastrophic forgetting when learning new categories. We propose a novel two-stage learning paradigm FOSTER, empowering the model to learn new categories adaptively.
  arXiv  Detail & Related papers  (2022-04-10T11:38:33Z)
• Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning [138.35405462309456]
  Non-exemplar class-incremental learning is to recognize both the old and new classes when old class samples cannot be saved. Our scheme consists of a structure reorganization strategy that fuses main-branch expansion and side-branch updating to maintain the old features. A prototype selection mechanism is proposed to enhance the discrimination between the old and new classes by selectively incorporating new samples into the distillation process.
  arXiv  Detail & Related papers  (2022-03-12T06:42:20Z)
• Self-Supervised Class Incremental Learning [51.62542103481908]
  Existing Class Incremental Learning (CIL) methods are based on a supervised classification framework sensitive to data labels. When updating them based on the new class data, they suffer from catastrophic forgetting: the model cannot discern old class data clearly from the new. In this paper, we explore the performance of Self-Supervised representation learning in Class Incremental Learning (SSCIL) for the first time.
  arXiv  Detail & Related papers  (2021-11-18T06:58:19Z)
• Class-incremental Learning with Rectified Feature-Graph Preservation [24.098892115785066]
  A central theme of this paper is to learn new classes that arrive in sequential phases over time. We propose a weighted-Euclidean regularization for old knowledge preservation. We show how it can work with binary cross-entropy to increase class separation for effective learning of new classes.
  arXiv  Detail & Related papers  (2020-12-15T07:26:04Z)
• Two-Level Residual Distillation based Triple Network for Incremental Object Detection [21.725878050355824]
  We propose a novel incremental object detector based on Faster R-CNN to continuously learn from new object classes without using old data. It is a triple network where an old model and a residual model as assistants for helping the incremental model learning on new classes without forgetting the previous learned knowledge.
  arXiv  Detail & Related papers  (2020-07-27T11:04:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.