Dynamic Feature Regularized Loss for Weakly Supervised Semantic Segmentation

• URL: http://arxiv.org/abs/2108.01296v1
• Date: Tue, 3 Aug 2021 05:11:00 GMT
• Title: Dynamic Feature Regularized Loss for Weakly Supervised Semantic Segmentation
• Authors: Bingfeng Zhang, Jimin Xiao, Yao Zhao
• Abstract summary: We propose a new regularized loss which utilizes both shallow and deep features that are dynamically updated. Our approach achieves new state-of-the-art performances, outperforming other approaches by a significant margin with more than 6% mIoU increase.
• Score: 37.43674181562307
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We focus on tackling weakly supervised semantic segmentation with scribble-level annotation. The regularized loss has been proven to be an effective solution for this task. However, most existing regularized losses only leverage static shallow features (color, spatial information) to compute the regularized kernel, which limits its final performance since such static shallow features fail to describe pair-wise pixel relationship in complicated cases. In this paper, we propose a new regularized loss which utilizes both shallow and deep features that are dynamically updated in order to aggregate sufficient information to represent the relationship of different pixels. Moreover, in order to provide accurate deep features, we adopt vision transformer as the backbone and design a feature consistency head to train the pair-wise feature relationship. Unlike most approaches that adopt multi-stage training strategy with many bells and whistles, our approach can be directly trained in an end-to-end manner, in which the feature consistency head and our regularized loss can benefit from each other. Extensive experiments show that our approach achieves new state-of-the-art performances, outperforming other approaches by a significant margin with more than 6\% mIoU increase.

Related papers

• Robust Network Learning via Inverse Scale Variational Sparsification [55.64935887249435]
  We introduce an inverse scale variational sparsification framework within a time-continuous inverse scale space formulation. Unlike frequency-based methods, our approach not only removes noise by smoothing small-scale features. We show the efficacy of our approach through enhanced robustness against various noise types.
  arXiv  Detail & Related papers  (2024-09-27T03:17:35Z)
• Deep Common Feature Mining for Efficient Video Semantic Segmentation [29.054945307605816]
  We present Deep Common Feature Mining (DCFM) for video semantic segmentation. DCFM explicitly decomposes features into two complementary components. We show that our method has a superior balance between accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-03-05T06:17:59Z)
• Mitigating the Effect of Incidental Correlations on Part-based Learning [50.682498099720114]
  Part-based representations could be more interpretable and generalize better with limited data. We present two innovative regularization methods for part-based representations. We exhibit state-of-the-art (SoTA) performance on few-shot learning tasks on benchmark datasets.
  arXiv  Detail & Related papers  (2023-09-30T13:44:48Z)
• A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images [15.75291664088815]
  A major issue concerning current deep neural architectures is known as catastrophic forgetting. We propose a contrastive regularization, where any given input is compared with its augmented version. We show the effectiveness of our solution on the Potsdam dataset, outperforming the incremental baseline in every test.
  arXiv  Detail & Related papers  (2021-12-07T16:44:45Z)
• Semi-supervised Semantic Segmentation with Directional Context-aware Consistency [66.49995436833667]
  We focus on the semi-supervised segmentation problem where only a small set of labeled data is provided with a much larger collection of totally unlabeled images. A preferred high-level representation should capture the contextual information while not losing self-awareness. We present the Directional Contrastive Loss (DC Loss) to accomplish the consistency in a pixel-to-pixel manner.
  arXiv  Detail & Related papers  (2021-06-27T03:42:40Z)
• InverseForm: A Loss Function for Structured Boundary-Aware Segmentation [80.39674800972182]
  We present a novel boundary-aware loss term for semantic segmentation using an inverse-transformation network. This plug-in loss term complements the cross-entropy loss in capturing boundary transformations. We analyze the quantitative and qualitative effects of our loss function on three indoor and outdoor segmentation benchmarks.
  arXiv  Detail & Related papers  (2021-04-06T18:52:45Z)
• Self-paced and self-consistent co-training for semi-supervised image segmentation [23.100800154116627]
  Deep co-training has been proposed as an effective approach for image segmentation when annotated data is scarce. In this paper, we improve existing approaches for semi-supervised segmentation with a self-paced and self-consistent co-training method.
  arXiv  Detail & Related papers  (2020-10-31T17:41:03Z)
• Deep Semantic Matching with Foreground Detection and Cycle-Consistency [103.22976097225457]
  We address weakly supervised semantic matching based on a deep network. We explicitly estimate the foreground regions to suppress the effect of background clutter. We develop cycle-consistent losses to enforce the predicted transformations across multiple images to be geometrically plausible and consistent.
  arXiv  Detail & Related papers  (2020-03-31T22:38:09Z)

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.