Dual Progressive Transformations for Weakly Supervised Semantic Segmentation

• URL: http://arxiv.org/abs/2209.15211v1
• Date: Fri, 30 Sep 2022 03:42:52 GMT
• Title: Dual Progressive Transformations for Weakly Supervised Semantic Segmentation
• Authors: Dongjian Huo, Yukun Su and Qingyao Wu
• Abstract summary: Weakly supervised semantic segmentation (WSSS) is a challenging task in computer vision. We propose a Convolutional Neural Networks Refined Transformer (CRT) to mine a globally complete and locally accurate class activation maps. Our proposed CRT achieves the new state-of-the-art performance on both the weakly supervised semantic segmentation task.
• Score: 23.68115323096787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly supervised semantic segmentation (WSSS), which aims to mine the object regions by merely using class-level labels, is a challenging task in computer vision. The current state-of-the-art CNN-based methods usually adopt Class-Activation-Maps (CAMs) to highlight the potential areas of the object, however, they may suffer from the part-activated issues. To this end, we try an early attempt to explore the global feature attention mechanism of vision transformer in WSSS task. However, since the transformer lacks the inductive bias as in CNN models, it can not boost the performance directly and may yield the over-activated problems. To tackle these drawbacks, we propose a Convolutional Neural Networks Refined Transformer (CRT) to mine a globally complete and locally accurate class activation maps in this paper. To validate the effectiveness of our proposed method, extensive experiments are conducted on PASCAL VOC 2012 and CUB-200-2011 datasets. Experimental evaluations show that our proposed CRT achieves the new state-of-the-art performance on both the weakly supervised semantic segmentation task the weakly supervised object localization task, which outperform others by a large margin.

Related papers

• 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)
• Visual Prompt Tuning in Null Space for Continual Learning [51.96411454304625]
  Existing prompt-tuning methods have demonstrated impressive performances in continual learning (CL) This paper aims to learn each task by tuning the prompts in the direction orthogonal to the subspace spanned by previous tasks' features. In practice, an effective null-space-based approximation solution has been proposed to implement the prompt gradient projection.
  arXiv  Detail & Related papers  (2024-06-09T05:57:40Z)
• Leveraging Swin Transformer for Local-to-Global Weakly Supervised Semantic Segmentation [12.103012959947055]
  This work explores the use of Swin Transformer by proposing "SWTformer" to enhance the accuracy of the initial seed CAMs. SWTformer-V1 achieves a 0.98% mAP higher localization accuracy, outperforming state-of-the-art models. SWTformer-V2 incorporates a multi-scale feature fusion mechanism to extract additional information.
  arXiv  Detail & Related papers  (2024-01-31T13:41:17Z)
• Dual-Augmented Transformer Network for Weakly Supervised Semantic Segmentation [4.02487511510606]
  Weakly supervised semantic segmentation (WSSS) is a fundamental computer vision task, which aims to segment out the object within only class-level labels. Traditional methods adopt the CNN-based network and utilize the class activation map (CAM) strategy to discover the object regions. An alternative is to explore vision transformers (ViT) to encode the image to acquire the global semantic information. We propose a dual network with both CNN-based and transformer networks for mutually complementary learning.
  arXiv  Detail & Related papers  (2023-09-30T08:41:11Z)
• Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation [84.62067728093358]
  Weakly supervised object localization and semantic segmentation aim to localize objects using only image-level labels. New paradigm has emerged by generating a foreground prediction map to achieve pixel-level localization. This paper presents two astonishing experimental observations on the object localization learning process.
  arXiv  Detail & Related papers  (2023-09-22T15:44:10Z)
• Semantic-Constraint Matching Transformer for Weakly Supervised Object Localization [31.039698757869974]
  Weakly supervised object localization (WSOL) strives to learn to localize objects with only image-level supervision. Previous CNN-based methods suffer from partial activation issues, concentrating on the object's discriminative part instead of the entire entity scope. We propose a novel Semantic-Constraint Matching Network (SCMN) via a transformer to converge on the divergent activation.
  arXiv  Detail & Related papers  (2023-09-04T03:20:31Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation [24.08326440298189]
  We propose a novel activation modulation and recalibration scheme for weakly supervised semantic segmentation. We show that AMR establishes a new state-of-the-art performance on the PASCAL VOC 2012 dataset. Experiments also reveal that our scheme is plug-and-play and can be incorporated with other approaches to boost their performance.
  arXiv  Detail & Related papers  (2021-12-16T16:26:14Z)
• TSG: Target-Selective Gradient Backprop for Probing CNN Visual Saliency [72.9106103283475]
  We study the visual saliency, a.k.a. visual explanation, to interpret convolutional neural networks. Inspired by those observations, we propose a novel visual saliency framework, termed Target-Selective Gradient (TSG) backprop. The proposed TSG consists of two components, namely, TSG-Conv and TSG-FC, which rectify the gradients for convolutional layers and fully-connected layers, respectively.
  arXiv  Detail & Related papers  (2021-10-11T12:00:20Z)
• Efficient Hybrid Transformer: Learning Global-local Context for Urban Sence Segmentation [11.237929167356725]
  We propose an efficient hybrid Transformer (EHT) for semantic segmentation of urban scene images. EHT takes advantage of CNNs and Transformer, learning global-local context to strengthen the feature representation. The proposed EHT achieves a 67.0% mIoU on the UAVid test set and outperforms other lightweight models significantly.
  arXiv  Detail & Related papers  (2021-09-18T13:55:38Z)
• Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation [93.83369981759996]
  We propose a self-supervised equivariant attention mechanism (SEAM) to discover additional supervision and narrow the gap. Our method is based on the observation that equivariance is an implicit constraint in fully supervised semantic segmentation. We propose consistency regularization on predicted CAMs from various transformed images to provide self-supervision for network learning.
  arXiv  Detail & Related papers  (2020-04-09T14:57:57Z)

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.