Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings

• URL: http://arxiv.org/abs/2202.02002v2
• Date: Tue, 30 Apr 2024 06:30:18 GMT
• Title: Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings
• Authors: Wei Yin, Yifan Liu, Chunhua Shen, Baichuan Sun, Anton van den Hengel,
• Abstract summary: We propose an approach to semantic segmentation that achieves state-of-the-art supervised performance when applied in a zero-shot setting. This is achieved by replacing each class label with a vector-valued embedding of a short paragraph that describes the class. The resulting merged semantic segmentation dataset of over 2 Million images enables training a model that achieves performance equal to that of state-of-the-art supervised methods on 7 benchmark datasets.
• Score: 81.09026586111811
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We propose an approach to semantic segmentation that achieves state-of-the-art supervised performance when applied in a zero-shot setting. It thus achieves results equivalent to those of the supervised methods, on each of the major semantic segmentation datasets, without training on those datasets. This is achieved by replacing each class label with a vector-valued embedding of a short paragraph that describes the class. The generality and simplicity of this approach enables merging multiple datasets from different domains, each with varying class labels and semantics. The resulting merged semantic segmentation dataset of over 2 Million images enables training a model that achieves performance equal to that of state-of-the-art supervised methods on 7 benchmark datasets, despite not using any images therefrom. By fine-tuning the model on standard semantic segmentation datasets, we also achieve a significant improvement over the state-of-the-art supervised segmentation on NYUD-V2 and PASCAL-context at 60% and 65% mIoU, respectively. Based on the closeness of language embeddings, our method can even segment unseen labels. Extensive experiments demonstrate strong generalization to unseen image domains and unseen labels, and that the method enables impressive performance improvements in downstream applications, including depth estimation and instance segmentation.

Related papers

• Scribbles for All: Benchmarking Scribble Supervised Segmentation Across Datasets [51.74296438621836]
  We introduce Scribbles for All, a label and training data generation algorithm for semantic segmentation trained on scribble labels. The main limitation of scribbles as source for weak supervision is the lack of challenging datasets for scribble segmentation. Scribbles for All provides scribble labels for several popular segmentation datasets and provides an algorithm to automatically generate scribble labels for any dataset with dense annotations.
  arXiv  Detail & Related papers  (2024-08-22T15:29:08Z)
• Multi-Scale Semantic Segmentation with Modified MBConv Blocks [29.026787888644474]
  We introduce a novel adaptation of MBConv blocks specifically tailored for semantic segmentation. By implementing these changes, our approach achieves impressive mean Intersection over Union (IoU) scores of 84.5% and 84.0% on the Cityscapes test and validation datasets.
  arXiv  Detail & Related papers  (2024-02-07T07:01:08Z)
• A Lightweight Clustering Framework for Unsupervised Semantic Segmentation [28.907274978550493]
  Unsupervised semantic segmentation aims to categorize each pixel in an image into a corresponding class without the use of annotated data. We propose a lightweight clustering framework for unsupervised semantic segmentation. Our framework achieves state-of-the-art results on PASCAL VOC and MS COCO datasets.
  arXiv  Detail & Related papers  (2023-11-30T15:33:42Z)
• ISLE: A Framework for Image Level Semantic Segmentation Ensemble [5.137284292672375]
  Conventional semantic segmentation networks require massive pixel-wise annotated labels to reach state-of-the-art prediction quality. We propose ISLE, which employs an ensemble of the "pseudo-labels" for a given set of different semantic segmentation techniques on a class-wise level. We reach up to 2.4% improvement over ISLE's individual components.
  arXiv  Detail & Related papers  (2023-03-14T13:36:36Z)
• Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding [95.78002228538841]
  We propose a new open-world semantic segmentation pipeline that makes the first attempt to learn to segment semantic objects of various open-world categories without any efforts on dense annotations. Our method can directly segment objects of arbitrary categories, outperforming zero-shot segmentation methods that require data labeling on three benchmark datasets.
  arXiv  Detail & Related papers  (2022-07-18T09:20:04Z)
• TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation [44.75300205362518]
  Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations. We propose the first top-down unsupervised semantic segmentation framework for fine-grained segmentation in extremely complicated scenarios. Our results show that our top-down unsupervised segmentation is robust to both object-centric and scene-centric datasets.
  arXiv  Detail & Related papers  (2021-12-02T18:59:03Z)
• Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation [94.16816278191477]
  We present a framework for semi-adaptive and domain-supervised semantic segmentation. It is enhanced by self-supervised monocular depth estimation trained only on unlabeled image sequences. We validate the proposed model on the Cityscapes dataset.
  arXiv  Detail & Related papers  (2021-08-28T01:33:38Z)
• Multi-dataset Pretraining: A Unified Model for Semantic Segmentation [97.61605021985062]
  We propose a unified framework, termed as Multi-Dataset Pretraining, to take full advantage of the fragmented annotations of different datasets. This is achieved by first pretraining the network via the proposed pixel-to-prototype contrastive loss over multiple datasets. In order to better model the relationship among images and classes from different datasets, we extend the pixel level embeddings via cross dataset mixing.
  arXiv  Detail & Related papers  (2021-06-08T06:13:11Z)
• Segmenter: Transformer for Semantic Segmentation [79.9887988699159]
  We introduce Segmenter, a transformer model for semantic segmentation. We build on the recent Vision Transformer (ViT) and extend it to semantic segmentation. It outperforms the state of the art on the challenging ADE20K dataset and performs on-par on Pascal Context and Cityscapes.
  arXiv  Detail & Related papers  (2021-05-12T13:01:44Z)
• Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank [5.967279020820772]
  We propose a novel representation learning module based on contrastive learning. This module enforces the segmentation network to yield similar pixel-level feature representations for same-class samples. In an end-to-end training, the features from both labeled and unlabeled data are optimized to be similar to same-class samples from the memory bank.
  arXiv  Detail & Related papers  (2021-04-27T18:19:33Z)

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.