Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement

• URL: http://arxiv.org/abs/2312.15731v4
• Date: Tue, 9 Jan 2024 14:59:23 GMT
• Title: Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement
• Authors: Jing Wang, Jinagyun Li, Chen Chen, Yisi Zhang, Haoran Shen, Tianxiang Zhang
• Abstract summary: The Few-Shot (FSS) aims to accomplish the novel class segmentation task with a few annotated images. We propose a novel framework based on the adapter mechanism, namely Adaptive FSS, which can efficiently adapt the existing FSS model to the novel classes. Our approach is compatible with diverse FSS methods with different backbones by simply inserting PAM between the layers of the encoder.
• Score: 6.197356908000006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Few-Shot Segmentation (FSS) aims to accomplish the novel class segmentation task with a few annotated images. Current FSS research based on meta-learning focus on designing a complex interaction mechanism between the query and support feature. However, unlike humans who can rapidly learn new things from limited samples, the existing approach relies solely on fixed feature matching to tackle new tasks, lacking adaptability. In this paper, we propose a novel framework based on the adapter mechanism, namely Adaptive FSS, which can efficiently adapt the existing FSS model to the novel classes. In detail, we design the Prototype Adaptive Module (PAM), which utilizes accurate category information provided by the support set to derive class prototypes, enhancing class-specific information in the multi-stage representation. In addition, our approach is compatible with diverse FSS methods with different backbones by simply inserting PAM between the layers of the encoder. Experiments demonstrate that our method effectively improves the performance of the FSS models (e.g., MSANet, HDMNet, FPTrans, and DCAMA) and achieve new state-of-the-art (SOTA) results (i.e., 72.4\% and 79.1\% mIoU on PASCAL-5$^i$ 1-shot and 5-shot settings, 52.7\% and 60.0\% mIoU on COCO-20$^i$ 1-shot and 5-shot settings). Our code can be available at https://github.com/jingw193/AdaptiveFSS.

Related papers

• TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation [40.49924427388922]
  We propose a task-adaptive auto-visual prompt framework for Cross-dominan Few-shot segmentation (CD-FSS) We incorporate a Class Domain Task-Adaptive Auto-Prompt (CDTAP) module to enable class-domain feature extraction and generate high-quality, learnable visual prompts. Our model outperforms the state-of-the-art CD-FSS approach, achieving an average accuracy improvement of 1.3% in the 1-shot setting and 11.76% in the 5-shot setting.
  arXiv  Detail & Related papers  (2024-09-09T07:43:58Z)
• Fine-Grained Prototypes Distillation for Few-Shot Object Detection [8.795211323408513]
  Few-shot object detection (FSOD) aims at extending a generic detector for novel object detection with only a few training examples. In general, methods based on meta-learning employ an additional support branch to encode novel examples into class prototypes. New methods are required to capture the distinctive local context for more robust novel object detection.
  arXiv  Detail & Related papers  (2024-01-15T12:12:48Z)
• Hierarchical Dense Correlation Distillation for Few-Shot Segmentation-Extended Abstract [47.85056124410376]
  Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations. We design Hierarchically Decoupled Matching Network (HDMNet) mining pixel-level support correlation based on the transformer architecture. We propose a matching module to reduce train-set overfitting and introduce correlation distillation leveraging semantic correspondence from coarse resolution to boost fine-grained segmentation.
  arXiv  Detail & Related papers  (2023-06-27T08:10:20Z)
• Multi-Modal Few-Shot Temporal Action Detection [157.96194484236483]
  Few-shot (FS) and zero-shot (ZS) learning are two different approaches for scaling temporal action detection to new classes. We introduce a new multi-modality few-shot (MMFS) TAD problem, which can be considered as a marriage of FS-TAD and ZS-TAD.
  arXiv  Detail & Related papers  (2022-11-27T18:13:05Z)
• Prediction Calibration for Generalized Few-shot Semantic Segmentation [101.69940565204816]
  Generalized Few-shot Semantic (GFSS) aims to segment each image pixel into either base classes with abundant training examples or novel classes with only a handful of (e.g., 1-5) training images per class. We build a cross-attention module that guides the classifier's final prediction using the fused multi-level features. Our PCN outperforms the state-the-art alternatives by large margins.
  arXiv  Detail & Related papers  (2022-10-15T13:30:12Z)
• 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)
• Learning What Not to Segment: A New Perspective on Few-Shot Segmentation [63.910211095033596]
  Recently few-shot segmentation (FSS) has been extensively developed. This paper proposes a fresh and straightforward insight to alleviate the problem. In light of the unique nature of the proposed approach, we also extend it to a more realistic but challenging setting.
  arXiv  Detail & Related papers  (2022-03-15T03:08:27Z)
• Novel Class Discovery in Semantic Segmentation [104.30729847367104]
  We introduce a new setting of Novel Class Discovery in Semantic (NCDSS) It aims at segmenting unlabeled images containing new classes given prior knowledge from a labeled set of disjoint classes. In NCDSS, we need to distinguish the objects and background, and to handle the existence of multiple classes within an image. We propose the Entropy-based Uncertainty Modeling and Self-training (EUMS) framework to overcome noisy pseudo-labels.
  arXiv  Detail & Related papers  (2021-12-03T13:31:59Z)
• APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation [56.387647750094466]
  Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only a few labeled support images. Most advanced solutions exploit a metric learning framework that performs segmentation through matching each query feature to a learned class-specific prototype. We present an adaptive prototype representation by introducing class-specific and class-agnostic prototypes.
  arXiv  Detail & Related papers  (2021-11-24T04:38:37Z)
• Modular Adaptation for Cross-Domain Few-Shot Learning [8.997255739981437]
  We show that substantial performance improvement of downstream tasks can be achieved by appropriate designs of the adaptation process. We propose a modular adaptation method that selectively performs multiple state-of-the-art (SOTA) adaptation methods in sequence. As different downstream tasks may require different types of adaptation, our modular adaptation enables the dynamic configuration of the most suitable modules.
  arXiv  Detail & Related papers  (2021-04-01T16:50:43Z)
• 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)

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.