Continual Attentive Fusion for Incremental Learning in Semantic Segmentation

• URL: http://arxiv.org/abs/2202.00432v1
• Date: Tue, 1 Feb 2022 14:38:53 GMT
• Title: Continual Attentive Fusion for Incremental Learning in Semantic Segmentation
• Authors: Guanglei Yang, Enrico Fini, Dan Xu, Paolo Rota, Mingli Ding, Hao Tang, Xavier Alameda-Pineda, Elisa Ricci
• Abstract summary: Deep architectures trained with gradient-based techniques suffer from catastrophic forgetting. We introduce a novel attentive feature distillation approach to mitigate catastrophic forgetting. We also introduce a novel strategy to account for the background class in the distillation loss, thus preventing biased predictions.
• Score: 43.98082955427662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over the past years, semantic segmentation, as many other tasks in computer vision, benefited from the progress in deep neural networks, resulting in significantly improved performance. However, deep architectures trained with gradient-based techniques suffer from catastrophic forgetting, which is the tendency to forget previously learned knowledge while learning new tasks. Aiming at devising strategies to counteract this effect, incremental learning approaches have gained popularity over the past years. However, the first incremental learning methods for semantic segmentation appeared only recently. While effective, these approaches do not account for a crucial aspect in pixel-level dense prediction problems, i.e. the role of attention mechanisms. To fill this gap, in this paper we introduce a novel attentive feature distillation approach to mitigate catastrophic forgetting while accounting for semantic spatial- and channel-level dependencies. Furthermore, we propose a {continual attentive fusion} structure, which takes advantage of the attention learned from the new and the old tasks while learning features for the new task. Finally, we also introduce a novel strategy to account for the background class in the distillation loss, thus preventing biased predictions. We demonstrate the effectiveness of our approach with an extensive evaluation on Pascal-VOC 2012 and ADE20K, setting a new state of the art.

Related papers

• FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding [28.880226459932146]
  This paper presents a novel Fairness Learning via Contrastive Attention Approach to continual learning in semantic scene understanding. We first introduce a new Fairness Contrastive Clustering loss to address the problems of catastrophic forgetting and fairness. Then, we propose an attention-based visual grammar approach to effectively model the background shift problem and unknown classes.
  arXiv  Detail & Related papers  (2023-11-27T16:07:39Z)
• Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation [46.14545656625703]
  catastrophic forgetting is the tendency of neural networks to fail to preserve the knowledge acquired from old tasks when learning new tasks. We propose a novel distillation framework, Uncertainty-aware Contrastive Distillation (method) Our results demonstrate the advantage of the proposed distillation technique, which can be used in synergy with previous IL approaches.
  arXiv  Detail & Related papers  (2022-03-26T15:32:12Z)
• On Generalizing Beyond Domains in Cross-Domain Continual Learning [91.56748415975683]
  Deep neural networks often suffer from catastrophic forgetting of previously learned knowledge after learning a new task. Our proposed approach learns new tasks under domain shift with accuracy boosts up to 10% on challenging datasets such as DomainNet and OfficeHome.
  arXiv  Detail & Related papers  (2022-03-08T09:57:48Z)
• Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation [39.025848280224785]
  We introduce a novel incremental class learning approach for semantic segmentation. Since each training step provides annotation only for a subset of all possible classes, pixels of the background class exhibit a semantic shift. We demonstrate the effectiveness of our approach with an extensive evaluation on the Pascal-VOC, ADE20K, and Cityscapes datasets.
  arXiv  Detail & Related papers  (2022-01-31T16:33:21Z)
• Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations [18.655840060559168]
  This paper focuses on class incremental continual learning in semantic segmentation. New categories are made available over time while previous training data is not retained. The proposed continual learning scheme shapes the latent space to reduce forgetting whilst improving the recognition of novel classes.
  arXiv  Detail & Related papers  (2021-03-10T21:02:05Z)
• Untangling tradeoffs between recurrence and self-attention in neural networks [81.30894993852813]
  We present a formal analysis of how self-attention affects gradient propagation in recurrent networks. We prove that it mitigates the problem of vanishing gradients when trying to capture long-term dependencies. We propose a relevancy screening mechanism that allows for a scalable use of sparse self-attention with recurrence.
  arXiv  Detail & Related papers  (2020-06-16T19:24:25Z)
• Continual Deep Learning by Functional Regularisation of Memorable Past [95.97578574330934]
  Continually learning new skills is important for intelligent systems, yet standard deep learning methods suffer from catastrophic forgetting of the past. We propose a new functional-regularisation approach that utilises a few memorable past examples crucial to avoid forgetting. Our method achieves state-of-the-art performance on standard benchmarks and opens a new direction for life-long learning where regularisation and memory-based methods are naturally combined.
  arXiv  Detail & Related papers  (2020-04-29T10:47:54Z)
• Incremental Object Detection via Meta-Learning [77.55310507917012]
  We propose a meta-learning approach that learns to reshape model gradients, such that information across incremental tasks is optimally shared. In comparison to existing meta-learning methods, our approach is task-agnostic, allows incremental addition of new-classes and scales to high-capacity models for object detection.
  arXiv  Detail & Related papers  (2020-03-17T13:40:00Z)

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.