Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation

• URL: http://arxiv.org/abs/2203.09653v1
• Date: Thu, 17 Mar 2022 23:29:03 GMT
• Title: Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation
• Authors: Tianfei Zhou, Meijie Zhang, Fang Zhao, Jianwu Li
• Abstract summary: We propose regional semantic contrast and aggregation (RCA) for learning semantic segmentation. RCA is equipped with a regional memory bank to store massive, diverse object patterns appearing in training data. RCA earns a strong capability of fine-grained semantic understanding, and eventually establishes new state-of-the-art results on two popular benchmarks.
• Score: 25.231470587575238
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning semantic segmentation from weakly-labeled (e.g., image tags only) data is challenging since it is hard to infer dense object regions from sparse semantic tags. Despite being broadly studied, most current efforts directly learn from limited semantic annotations carried by individual image or image pairs, and struggle to obtain integral localization maps. Our work alleviates this from a novel perspective, by exploring rich semantic contexts synergistically among abundant weakly-labeled training data for network learning and inference. In particular, we propose regional semantic contrast and aggregation (RCA) . RCA is equipped with a regional memory bank to store massive, diverse object patterns appearing in training data, which acts as strong support for exploration of dataset-level semantic structure. Particularly, we propose i) semantic contrast to drive network learning by contrasting massive categorical object regions, leading to a more holistic object pattern understanding, and ii) semantic aggregation to gather diverse relational contexts in the memory to enrich semantic representations. In this manner, RCA earns a strong capability of fine-grained semantic understanding, and eventually establishes new state-of-the-art results on two popular benchmarks, i.e., PASCAL VOC 2012 and COCO 2014.

Related papers

• Spatial Semantic Recurrent Mining for Referring Image Segmentation [63.34997546393106]
  We propose Stextsuperscript2RM to achieve high-quality cross-modality fusion. It follows a working strategy of trilogy: distributing language feature, spatial semantic recurrent coparsing, and parsed-semantic balancing. Our proposed method performs favorably against other state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2024-05-15T00:17:48Z)
• Leveraging Hidden Positives for Unsupervised Semantic Segmentation [5.937673383513695]
  We leverage contrastive learning by excavating hidden positives to learn rich semantic relationships. We introduce a gradient propagation strategy to learn semantic consistency between adjacent patches. Our proposed method achieves new state-of-the-art (SOTA) results in COCO-stuff, Cityscapes, and Potsdam-3 datasets.
  arXiv  Detail & Related papers  (2023-03-27T08:57:28Z)
• CTNet: Context-based Tandem Network for Semantic Segmentation [77.4337867789772]
  This work proposes a novel Context-based Tandem Network (CTNet) by interactively exploring the spatial contextual information and the channel contextual information. To further improve the performance of the learned representations for semantic segmentation, the results of the two context modules are adaptively integrated.
  arXiv  Detail & Related papers  (2021-04-20T07:33:11Z)
• Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation [53.49821324597837]
  Weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years. We present a Context Decoupling Augmentation ( CDA) method to change the inherent context in which the objects appear. To validate the effectiveness of the proposed method, extensive experiments on PASCAL VOC 2012 dataset with several alternative network architectures demonstrate that CDA can boost various popular WSSS methods to the new state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2021-03-02T15:05:09Z)
• EDS-MEMBED: Multi-sense embeddings based on enhanced distributional semantic structures via a graph walk over word senses [0.0]
  We leverage the rich semantic structures in WordNet to enhance the quality of multi-sense embeddings. We derive new distributional semantic similarity measures for M-SE from prior ones. We report evaluation results on 11 benchmark datasets involving WSD and Word Similarity tasks.
  arXiv  Detail & Related papers  (2021-02-27T14:36:55Z)
• Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification [2.963101656293054]
  Deep convolutional neural networks (CNNs) have shown a strong ability in mining discriminative object pose and parts information for image recognition. We propose a novel context-aware attentional pooling (CAP) that effectively captures subtle changes via sub-pixel gradients. We evaluate our approach using six state-of-the-art (SotA) backbone networks and eight benchmark datasets.
  arXiv  Detail & Related papers  (2021-01-17T10:15:02Z)
• Joint Semantic Analysis with Document-Level Cross-Task Coherence Rewards [13.753240692520098]
  We present a neural network architecture for joint coreference resolution and semantic role labeling for English. We use reinforcement learning to encourage global coherence over the document and between semantic annotations. This leads to improvements on both tasks in multiple datasets from different domains.
  arXiv  Detail & Related papers  (2020-10-12T09:36:24Z)
• GINet: Graph Interaction Network for Scene Parsing [58.394591509215005]
  We propose a Graph Interaction unit (GI unit) and a Semantic Context Loss (SC-loss) to promote context reasoning over image regions. The proposed GINet outperforms the state-of-the-art approaches on the popular benchmarks, including Pascal-Context and COCO Stuff.
  arXiv  Detail & Related papers  (2020-09-14T02:52:45Z)
• Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation [128.03739769844736]
  Two neural co-attentions are incorporated into the classifier to capture cross-image semantic similarities and differences. In addition to boosting object pattern learning, the co-attention can leverage context from other related images to improve localization map inference. Our algorithm sets new state-of-the-arts on all these settings, demonstrating well its efficacy and generalizability.
  arXiv  Detail & Related papers  (2020-07-03T21:53:46Z)
• Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning [86.45526827323954]
  Weakly-supervised semantic segmentation is a challenging task as no pixel-wise label information is provided for training. We propose an iterative algorithm to learn such pairwise relations. We show that the proposed algorithm performs favorably against the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-02-19T10:32:03Z)

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.