Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network

• URL: http://arxiv.org/abs/2301.03194v1
• Date: Mon, 9 Jan 2023 08:00:01 GMT
• Title: Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network
• Authors: Jie Liu, Yanqi Bao, Wenzhe Ying, Haochen Wang, Yang Gao, Jan-Jakob Sonke, Efstratios Gavves
• Abstract summary: Few-shot semantic segmentation (FSS) aims to achieve novel objects segmentation with only a few annotated samples. We propose a Support-induced Graph Convolutional Network (SiGCN) to explicitly excavate latent context structure in query images.
• Score: 28.46908214462594
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot semantic segmentation (FSS) aims to achieve novel objects segmentation with only a few annotated samples and has made great progress recently. Most of the existing FSS models focus on the feature matching between support and query to tackle FSS. However, the appearance variations between objects from the same category could be extremely large, leading to unreliable feature matching and query mask prediction. To this end, we propose a Support-induced Graph Convolutional Network (SiGCN) to explicitly excavate latent context structure in query images. Specifically, we propose a Support-induced Graph Reasoning (SiGR) module to capture salient query object parts at different semantic levels with a Support-induced GCN. Furthermore, an instance association (IA) module is designed to capture high-order instance context from both support and query instances. By integrating the proposed two modules, SiGCN can learn rich query context representation, and thus being more robust to appearance variations. Extensive experiments on PASCAL-5i and COCO-20i demonstrate that our SiGCN achieves state-of-the-art performance.

Related papers

• Beyond Mask: Rethinking Guidance Types in Few-shot Segmentation [67.35274834837064]
  We develop a universal vision-language framework (UniFSS) to integrate prompts from text, mask, box, and image. UniFSS significantly outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-16T08:41:01Z)
• Embedding Generalized Semantic Knowledge into Few-Shot Remote Sensing Segmentation [26.542268630980814]
  Few-shot segmentation (FSS) for remote sensing (RS) imagery leverages supporting information from limited annotated samples to achieve query segmentation of novel classes. Previous efforts are dedicated to mining segmentation-guiding visual cues from a constrained set of support samples. We propose a holistic semantic embedding (HSE) approach that effectively harnesses general semantic knowledge.
  arXiv  Detail & Related papers  (2024-05-22T14:26:04Z)
• Boosting Few-Shot Segmentation via Instance-Aware Data Augmentation and Local Consensus Guided Cross Attention [7.939095881813804]
  Few-shot segmentation aims to train a segmentation model that can fast adapt to a novel task for which only a few annotated images are provided. We introduce an instance-aware data augmentation (IDA) strategy that augments the support images based on the relative sizes of the target objects. The proposed IDA effectively increases the support set's diversity and promotes the distribution consistency between support and query images.
  arXiv  Detail & Related papers  (2024-01-18T10:29:10Z)
• Few-shot Medical Image Segmentation via Cross-Reference Transformer [3.2634122554914]
  Few-shot segmentation(FSS) has the potential to address these challenges by learning new categories from a small number of labeled samples. We propose a novel self-supervised few shot medical image segmentation network with Cross-Reference Transformer. Experimental results show that the proposed model achieves good results on both CT dataset and MRI dataset.
  arXiv  Detail & Related papers  (2023-04-19T13:05:18Z)
• Multi-Content Interaction Network for Few-Shot Segmentation [37.80624074068096]
  Few-Shot COCO is challenging for limited support images and large intra-class appearance discrepancies. We propose a Multi-Content Interaction Network (MCINet) to remedy this issue. MCINet improves FSS by incorporating the low-level structural information from another query branch into the high-level semantic features.
  arXiv  Detail & Related papers  (2023-03-11T04:21:59Z)
• Support-set based Multi-modal Representation Enhancement for Video Captioning [121.70886789958799]
  We propose a Support-set based Multi-modal Representation Enhancement (SMRE) model to mine rich information in a semantic subspace shared between samples. Specifically, we propose a Support-set Construction (SC) module to construct a support-set to learn underlying connections between samples and obtain semantic-related visual elements. During this process, we design a Semantic Space Transformation (SST) module to constrain relative distance and administrate multi-modal interactions in a self-supervised way.
  arXiv  Detail & Related papers  (2022-05-19T03:40:29Z)
• Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification [68.06496970320595]
  Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. We present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs.
  arXiv  Detail & Related papers  (2022-05-18T07:47:01Z)
• Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation [33.93192093090601]
  Key challenge for few-shot semantic segmentation (FSS) is how to tailor a desirable interaction among support and query features. We propose a prototype prototype convolution network (DPCN) to fully capture the intrinsic details for accurate FSS. Our DPCN is also flexible and efficient under the k-shot FSS setting.
  arXiv  Detail & Related papers  (2022-04-22T11:12:37Z)
• Boosting Few-shot Semantic Segmentation with Transformers [81.43459055197435]
  TRansformer-based Few-shot Semantic segmentation method (TRFS) Our model consists of two modules: Global Enhancement Module (GEM) and Local Enhancement Module (LEM)
  arXiv  Detail & Related papers  (2021-08-04T20:09:21Z)
• CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection [91.91911418421086]
  Co-Salient Object Detection (CoSOD) aims at discovering salient objects that repeatedly appear in a given query group containing two or more relevant images. One challenging issue is how to effectively capture co-saliency cues by modeling and exploiting inter-image relationships. We present an end-to-end collaborative aggregation-and-distribution network (CoADNet) to capture both salient and repetitive visual patterns from multiple images.
  arXiv  Detail & Related papers  (2020-11-10T04:28:11Z)
• Generalized Few-shot Semantic Segmentation [68.69434831359669]
  We introduce a new benchmark called Generalized Few-Shot Semantic (GFS-Seg) to analyze the ability of simultaneously segmenting the novel categories. It is the first study showing that previous representative state-of-the-art generalizations fall short in GFS-Seg. We propose the Context-Aware Prototype Learning (CAPL) that significantly improves performance by 1) leveraging the co-occurrence prior knowledge from support samples, and 2) dynamically enriching contextual information to the conditioned, on the content of each query image.
  arXiv  Detail & Related papers  (2020-10-11T10:13:21Z)

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.