DreamMask: Boosting Open-vocabulary Panoptic Segmentation with Synthetic Data

• URL: http://arxiv.org/abs/2501.02048v1
• Date: Fri, 03 Jan 2025 19:00:00 GMT
• Title: DreamMask: Boosting Open-vocabulary Panoptic Segmentation with Synthetic Data
• Authors: Yuanpeng Tu, Xi Chen, Ser-Nam Lim, Hengshuang Zhao,
• Abstract summary: We propose DreamMask, which explores how to generate training data in the open-vocabulary setting, and how to train the model with both real and synthetic data. In general, DreamMask significantly simplifies the collection of large-scale training data, serving as a plug-and-play enhancement for existing methods. For instance, when trained on COCO and tested on ADE20K, the model equipped with DreamMask outperforms the previous state-of-the-art by a substantial margin of 2.1% mIoU.
• Score: 61.62554324594797
• License:
• Abstract: Open-vocabulary panoptic segmentation has received significant attention due to its applicability in the real world. Despite claims of robust generalization, we find that the advancements of previous works are attributed mainly on trained categories, exposing a lack of generalization to novel classes. In this paper, we explore boosting existing models from a data-centric perspective. We propose DreamMask, which systematically explores how to generate training data in the open-vocabulary setting, and how to train the model with both real and synthetic data. For the first part, we propose an automatic data generation pipeline with off-the-shelf models. We propose crucial designs for vocabulary expansion, layout arrangement, data filtering, etc. Equipped with these techniques, our generated data could significantly outperform the manually collected web data. To train the model with generated data, a synthetic-real alignment loss is designed to bridge the representation gap, bringing noticeable improvements across multiple benchmarks. In general, DreamMask significantly simplifies the collection of large-scale training data, serving as a plug-and-play enhancement for existing methods. For instance, when trained on COCO and tested on ADE20K, the model equipped with DreamMask outperforms the previous state-of-the-art by a substantial margin of 2.1% mIoU.

Related papers

• SMPLest-X: Ultimate Scaling for Expressive Human Pose and Shape Estimation [81.36747103102459]
  Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications. Current state-of-the-art methods focus on training innovative architectural designs on confined datasets. We investigate the impact of scaling up EHPS towards a family of generalist foundation models.
  arXiv  Detail & Related papers  (2025-01-16T18:59:46Z)
• Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data [7.603659241572307]
  We propose a novel UCB-based training procedure combined with a dynamic usability metric. Our proposed metric integrates low-level and high-level information from synthetic images and their corresponding real and synthetic datasets. We show that our metric is an effective way to rank synthetic images based on their usability.
  arXiv  Detail & Related papers  (2024-12-06T23:36:36Z)
• Rethinking Data Synthesis: A Teacher Model Training Recipe with Interpretation [12.736045604858738]
  Recent advances in large language model (LLM) training have highlighted the need for diverse, high-quality instruction data. We propose a paradigm shift named textbfNOMAD by investigating how to specifically train models for data generation.
  arXiv  Detail & Related papers  (2024-10-27T07:38:39Z)
• POINTS: Improving Your Vision-language Model with Affordable Strategies [28.611705477757454]
  We train a robust baseline model using latest advancements in vision-language models. We filter pre-training data using perplexity, selecting the lowest perplexity data for training. During visual instruction tuning, we used model soup on different datasets when adding more datasets yielded marginal improvements.
  arXiv  Detail & Related papers  (2024-09-07T13:41:37Z)
• Generative Expansion of Small Datasets: An Expansive Graph Approach [13.053285552524052]
  We introduce an Expansive Synthesis model generating large-scale, information-rich datasets from minimal samples. An autoencoder with self-attention layers and optimal transport refines distributional consistency. Results show comparable performance, demonstrating the model's potential to augment training data effectively.
  arXiv  Detail & Related papers  (2024-06-25T02:59:02Z)
• Distribution-Aware Data Expansion with Diffusion Models [55.979857976023695]
  We propose DistDiff, a training-free data expansion framework based on the distribution-aware diffusion model. DistDiff consistently enhances accuracy across a diverse range of datasets compared to models trained solely on original data.
  arXiv  Detail & Related papers  (2024-03-11T14:07:53Z)
• Data-efficient Large Vision Models through Sequential Autoregression [58.26179273091461]
  We develop an efficient, autoregression-based vision model on a limited dataset. We demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding. Our empirical evaluations underscore the model's agility in adapting to various tasks, heralding a significant reduction in the parameter footprint.
  arXiv  Detail & Related papers  (2024-02-07T13:41:53Z)
• TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments [84.6017003787244]
  This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
  arXiv  Detail & Related papers  (2022-08-16T20:46:08Z)
• Improving the Performance of Fine-Grain Image Classifiers via Generative Data Augmentation [0.5161531917413706]
  We develop Data Augmentation from Proficient Pre-Training of Robust Generative Adrial Networks (DAPPER GAN) DAPPER GAN is an ML analytics support tool that automatically generates novel views of training images. We experimentally evaluate this technique on the Stanford Cars dataset, demonstrating improved vehicle make and model classification accuracy.
  arXiv  Detail & Related papers  (2020-08-12T15:29:11Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)

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.