Prior-Enhanced Few-Shot Segmentation with Meta-Prototypes

• URL: http://arxiv.org/abs/2106.00572v1
• Date: Tue, 1 Jun 2021 15:34:30 GMT
• Title: Prior-Enhanced Few-Shot Segmentation with Meta-Prototypes
• Authors: Jian-Wei Zhang, Lei Lv, Yawei Luo, Hao-Zhe Feng, Yi Yang, Wei Chen
• Abstract summary: Few-shot segmentation(FSS) performance has been extensively promoted by introducing episodic training and class-wise prototypes. We propose the Prior-Enhanced network with Meta-Prototypes to tackle these limitations. Our method achieves the mean-IoU scores of 60.79% and 41.16% on PASCAL-$5i$ and COCO-$20i$, outperforming the state-of-the-art method by 3.49% and 5.64% in the 5-shot setting.
• Score: 32.898636584823215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot segmentation~(FSS) performance has been extensively promoted by introducing episodic training and class-wise prototypes. However, the FSS problem remains challenging due to three limitations: (1) Models are distracted by task-unrelated information; (2) The representation ability of a single prototype is limited; (3) Class-related prototypes ignore the prior knowledge of base classes. We propose the Prior-Enhanced network with Meta-Prototypes to tackle these limitations. The prior-enhanced network leverages the support and query (pseudo-) labels in feature extraction, which guides the model to focus on the task-related features of the foreground objects, and suppress much noise due to the lack of supervised knowledge. Moreover, we introduce multiple meta-prototypes to encode hierarchical features and learn class-agnostic structural information. The hierarchical features help the model highlight the decision boundary and focus on hard pixels, and the structural information learned from base classes is treated as the prior knowledge for novel classes. Experiments show that our method achieves the mean-IoU scores of 60.79% and 41.16% on PASCAL-$5^i$ and COCO-$20^i$, outperforming the state-of-the-art method by 3.49% and 5.64% in the 5-shot setting. Moreover, comparing with 1-shot results, our method promotes 5-shot accuracy by 3.73% and 10.32% on the above two benchmarks. The source code of our method is available at https://github.com/Jarvis73/PEMP.

Related papers

• EMPL: A novel Efficient Meta Prompt Learning Framework for Few-shot Unsupervised Domain Adaptation [22.586094394391747]
  We propose a novel Efficient Meta Prompt Learning Framework for FS-UDA. Within this framework, we use pre-trained CLIP model as the feature learning base model. Our method has the large improvement of at least 15.4% on 5-way 1-shot and 8.7% on 5-way 5-shot, compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-04T17:13:06Z)
• SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object Detection [2.0755366440393743]
  Confusion and forgetting of object classes have been challenges of prime interest in Few-Shot Object Detection (FSOD) We introduce a novel Submodular Mutual Information Learning framework which adopts mutual information functions. Our proposed approach generalizes to several existing approaches in FSOD, agnostic of the backbone architecture.
  arXiv  Detail & Related papers  (2024-07-02T20:53:43Z)
• Enhancing Visual Continual Learning with Language-Guided Supervision [76.38481740848434]
  Continual learning aims to empower models to learn new tasks without forgetting previously acquired knowledge. We argue that the scarce semantic information conveyed by the one-hot labels hampers the effective knowledge transfer across tasks. Specifically, we use PLMs to generate semantic targets for each class, which are frozen and serve as supervision signals.
  arXiv  Detail & Related papers  (2024-03-24T12:41:58Z)
• Rethinking Few-shot 3D Point Cloud Semantic Segmentation [62.80639841429669]
  This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS) We focus on two significant issues in the state-of-the-art: foreground leakage and sparse point distribution. To address these issues, we introduce a standardized FS-PCS setting, upon which a new benchmark is built.
  arXiv  Detail & Related papers  (2024-03-01T15:14:47Z)
• CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification [11.894289991529496]
  Few-shot classification is a challenging problem that aims to learn a model that can adapt to unseen classes given a few labeled samples. Recent approaches pre-train a feature extractor, and then fine-tune for episodic meta-learning. We propose a strategy to cross-attend and re-weight discriminative features for few-shot classification.
  arXiv  Detail & Related papers  (2022-03-25T06:14:51Z)
• Pack Together: Entity and Relation Extraction with Levitated Marker [61.232174424421025]
  We propose a novel span representation approach, named Packed Levitated Markers, to consider the dependencies between the spans (pairs) by strategically packing the markers in the encoder. Our experiments show that our model with packed levitated markers outperforms the sequence labeling model by 0.4%-1.9% F1 on three flat NER tasks, and beats the token concat model on six NER benchmarks.
  arXiv  Detail & Related papers  (2021-09-13T15:38:13Z)
• Learning Meta-class Memory for Few-Shot Semantic Segmentation [90.28474742651422]
  We introduce the concept of meta-class, which is the meta information shareable among all classes. We propose a novel Meta-class Memory based few-shot segmentation method (MM-Net), where we introduce a set of learnable memory embeddings. Our proposed MM-Net achieves 37.5% mIoU on the COCO dataset in 1-shot setting, which is 5.1% higher than the previous state-of-the-art.
  arXiv  Detail & Related papers  (2021-08-06T06:29:59Z)
• Prior Guided Feature Enrichment Network for Few-Shot Segmentation [64.91560451900125]
  State-of-the-art semantic segmentation methods require sufficient labeled data to achieve good results. Few-shot segmentation is proposed to tackle this problem by learning a model that quickly adapts to new classes with a few labeled support samples. Theses frameworks still face the challenge of generalization ability reduction on unseen classes due to inappropriate use of high-level semantic information.
  arXiv  Detail & Related papers  (2020-08-04T10:41:32Z)
• Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation [63.98724740606457]
  We present a joint effort of four groups, namely GT, USTC, Tencent, and UKE, to tackle Task 1 - Acoustic Scene Classification (ASC) in the DCASE 2020 Challenge. Task 1a focuses on ASC of audio signals recorded with multiple (real and simulated) devices into ten different fine-grained classes. Task 1b concerns with classification of data into three higher-level classes using low-complexity solutions.
  arXiv  Detail & Related papers  (2020-07-16T15:07:14Z)

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.