An Empirical Study on Multi-Domain Robust Semantic Segmentation

• URL: http://arxiv.org/abs/2212.04221v1
• Date: Thu, 8 Dec 2022 12:04:01 GMT
• Title: An Empirical Study on Multi-Domain Robust Semantic Segmentation
• Authors: Yajie Liu, Pu Ge, Qingjie Liu, Shichao Fan and Yunhong Wang
• Abstract summary: We train a unified model that is expected to perform well across domains on several popularity segmentation datasets. Our solution ranks 2nd on RVC 2022 semantic segmentation task, with a dataset only 1/3 size of the 1st model used.
• Score: 42.79166534691889
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How to effectively leverage the plentiful existing datasets to train a robust and high-performance model is of great significance for many practical applications. However, a model trained on a naive merge of different datasets tends to obtain poor performance due to annotation conflicts and domain divergence.In this paper, we attempt to train a unified model that is expected to perform well across domains on several popularity segmentation datasets.We conduct a detailed analysis of the impact on model generalization from three aspects of data augmentation, training strategies, and model capacity.Based on the analysis, we propose a robust solution that is able to improve model generalization across domains.Our solution ranks 2nd on RVC 2022 semantic segmentation task, with a dataset only 1/3 size of the 1st model used.

Related papers

• Robustness Analysis on Foundational Segmentation Models [28.01242494123917]
  In this work, we perform a robustness analysis of Visual Foundation Models (VFMs) for segmentation tasks. We benchmark seven state-of-the-art segmentation architectures using 2 different datasets. Our findings reveal several key insights: VFMs exhibit vulnerabilities to compression-induced corruptions, despite not outpacing all of unimodal models in robustness, multimodal models show competitive resilience in zero-shot scenarios, and VFMs demonstrate enhanced robustness for certain object categories.
  arXiv  Detail & Related papers  (2023-06-15T16:59:42Z)
• SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation [62.889835139583965]
  We introduce an unsupervised auxiliary task of learning an implicit underlying surface representation simultaneously on source and target data. As both domains share the same latent representation, the model is forced to accommodate discrepancies between the two sources of data. Our experiments demonstrate that our method achieves a better performance than the current state of the art, both in real-to-real and synthetic-to-real scenarios.
  arXiv  Detail & Related papers  (2023-04-06T17:36:23Z)
• Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework [74.25493157757943]
  We formulate a novel clustering model, which exploits the non-negative feature property and incorporates the multi-view information into a unified joint learning framework. We also explore, for the first time, the multi-model non-negative graph-based approach to clustering data based on deep features.
  arXiv  Detail & Related papers  (2022-11-03T08:18:27Z)
• Learning to Augment via Implicit Differentiation for Domain Generalization [107.9666735637355]
  Domain generalization (DG) aims to overcome the problem by leveraging multiple source domains to learn a domain-generalizable model. In this paper, we propose a novel augmentation-based DG approach, dubbed AugLearn. AugLearn shows effectiveness on three standard DG benchmarks, PACS, Office-Home and Digits-DG.
  arXiv  Detail & Related papers  (2022-10-25T18:51:51Z)
• MSeg: A Composite Dataset for Multi-domain Semantic Segmentation [100.17755160696939]
  We present MSeg, a composite dataset that unifies semantic segmentation datasets from different domains. We reconcile the generalization and bring the pixel-level annotations into alignment by relabeling more than 220,000 object masks in more than 80,000 images. A model trained on MSeg ranks first on the WildDash-v1 leaderboard for robust semantic segmentation, with no exposure to WildDash data during training.
  arXiv  Detail & Related papers  (2021-12-27T16:16:35Z)
• On Generalization in Coreference Resolution [66.05112218880907]
  We consolidate a set of 8 coreference resolution datasets targeting different domains to evaluate the off-the-shelf performance of models. We then mix three datasets for training; even though their domain, annotation guidelines, and metadata differ, we propose a method for jointly training a single model. We find that in a zero-shot setting, models trained on a single dataset transfer poorly while joint training yields improved overall performance.
  arXiv  Detail & Related papers  (2021-09-20T16:33:22Z)
• A Topological-Framework to Improve Analysis of Machine Learning Model Performance [5.3893373617126565]
  We propose a framework for evaluating machine learning models in which a dataset is treated as a "space" on which a model operates. We describe a topological data structure, presheaves, which offer a convenient way to store and analyze model performance between different subpopulations.
  arXiv  Detail & Related papers  (2021-07-09T23:11:13Z)

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.