SimCS: Simulation for Domain Incremental Online Continual Segmentation

• URL: http://arxiv.org/abs/2211.16234v2
• Date: Thu, 15 Feb 2024 12:12:05 GMT
• Title: SimCS: Simulation for Domain Incremental Online Continual Segmentation
• Authors: Motasem Alfarra, Zhipeng Cai, Adel Bibi, Bernard Ghanem, Matthias M\"uller
• Abstract summary: Existing continual learning approaches mostly focus on image classification in the class-incremental setup. We propose SimCS, a parameter-free method complementary to existing ones that uses simulated data to regularize continual learning.
• Score: 60.18777113752866
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continual Learning is a step towards lifelong intelligence where models continuously learn from recently collected data without forgetting previous knowledge. Existing continual learning approaches mostly focus on image classification in the class-incremental setup with clear task boundaries and unlimited computational budget. This work explores the problem of Online Domain-Incremental Continual Segmentation (ODICS), where the model is continually trained over batches of densely labeled images from different domains, with limited computation and no information about the task boundaries. ODICS arises in many practical applications. In autonomous driving, this may correspond to the realistic scenario of training a segmentation model over time on a sequence of cities. We analyze several existing continual learning methods and show that they perform poorly in this setting despite working well in class-incremental segmentation. We propose SimCS, a parameter-free method complementary to existing ones that uses simulated data to regularize continual learning. Experiments show that SimCS provides consistent improvements when combined with different CL methods.

Related papers

• A Practitioner's Guide to Continual Multimodal Pretraining [83.63894495064855]
  Multimodal foundation models serve numerous applications at the intersection of vision and language. To keep models updated, research into continual pretraining mainly explores scenarios with either infrequent, indiscriminate updates on large-scale new data, or frequent, sample-level updates. We introduce FoMo-in-Flux, a continual multimodal pretraining benchmark with realistic compute constraints and practical deployment requirements.
  arXiv  Detail & Related papers  (2024-08-26T17:59:01Z)
• ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning [54.68180752416519]
  Panoptic segmentation is a cutting-edge computer vision task. We introduce a novel and efficient method for continual panoptic segmentation based on Visual Prompt Tuning, dubbed ECLIPSE. Our approach involves freezing the base model parameters and fine-tuning only a small set of prompt embeddings, addressing both catastrophic forgetting and plasticity.
  arXiv  Detail & Related papers  (2024-03-29T11:31:12Z)
• Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning [68.16998247593209]
  offline reinforcement learning (RL) paradigm provides recipe to convert static behavior datasets into policies that can perform better than the policy that collected the data. In this paper, we propose an adaptive scheme for action quantization. We show that several state-of-the-art offline RL methods such as IQL, CQL, and BRAC improve in performance on benchmarks when combined with our proposed discretization scheme.
  arXiv  Detail & Related papers  (2023-10-18T06:07:10Z)
• Continual Vision-Language Representation Learning with Off-Diagonal Information [112.39419069447902]
  Multi-modal contrastive learning frameworks like CLIP typically require a large amount of image-text samples for training. This paper discusses the feasibility of continual CLIP training using streaming data.
  arXiv  Detail & Related papers  (2023-05-11T08:04:46Z)
• Building a Subspace of Policies for Scalable Continual Learning [21.03369477853538]
  We introduce Continual Subspace of Policies (CSP), a new approach that incrementally builds a subspace of policies for training a reinforcement learning agent on a sequence of tasks. CSP outperforms a number of popular baselines on a wide range of scenarios from two challenging domains, Brax (locomotion) and Continual World (manipulation)
  arXiv  Detail & Related papers  (2022-11-18T14:59:42Z)
• Learning an evolved mixture model for task-free continual learning [11.540150938141034]
  We address the Task-Free Continual Learning (TFCL) in which a model is trained on non-stationary data streams with no explicit task information. We introduce two simple dropout mechanisms to selectively remove stored examples in order to avoid memory overload.
  arXiv  Detail & Related papers  (2022-07-11T16:01:27Z)
• vCLIMB: A Novel Video Class Incremental Learning Benchmark [53.90485760679411]
  We introduce vCLIMB, a novel video continual learning benchmark. vCLIMB is a standardized test-bed to analyze catastrophic forgetting of deep models in video continual learning. We propose a temporal consistency regularization that can be applied on top of memory-based continual learning methods.
  arXiv  Detail & Related papers  (2022-01-23T22:14:17Z)
• Online Constrained Model-based Reinforcement Learning [13.362455603441552]
  Key requirement is the ability to handle continuous state and action spaces while remaining within a limited time and resource budget. We propose a model based approach that combines Gaussian Process regression and Receding Horizon Control. We test our approach on a cart pole swing-up environment and demonstrate the benefits of online learning on an autonomous racing task.
  arXiv  Detail & Related papers  (2020-04-07T15:51:34Z)

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.