Demonstration of MaskSearch: Efficiently Querying Image Masks for Machine Learning Workflows

• URL: http://arxiv.org/abs/2404.06563v1
• Date: Tue, 9 Apr 2024 18:27:59 GMT
• Title: Demonstration of MaskSearch: Efficiently Querying Image Masks for Machine Learning Workflows
• Authors: Lindsey Linxi Wei, Chung Yik Edward Yeung, Hongjian Yu, Jingchuan Zhou, Dong He, Magdalena Balazinska,
• Abstract summary: MaskSearch is a system designed to accelerate queries over databases of image masks generated by machine learning models. It formalizes and accelerates a new category of queries for retrieving images and their corresponding masks based on mask properties.
• Score: 3.7576528194977965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We demonstrate MaskSearch, a system designed to accelerate queries over databases of image masks generated by machine learning models. MaskSearch formalizes and accelerates a new category of queries for retrieving images and their corresponding masks based on mask properties, which support various applications, from identifying spurious correlations learned by models to exploring discrepancies between model saliency and human attention. This demonstration makes the following contributions:(1) the introduction of MaskSearch's graphical user interface (GUI), which enables interactive exploration of image databases through mask properties, (2) hands-on opportunities for users to explore MaskSearch's capabilities and constraints within machine learning workflows, and (3) an opportunity for conference attendees to understand how MaskSearch accelerates queries over image masks.

Related papers

• Masked Image Modeling: A Survey [73.21154550957898]
  Masked image modeling emerged as a powerful self-supervised learning technique in computer vision. We construct a taxonomy and review the most prominent papers in recent years. We aggregate the performance results of various masked image modeling methods on the most popular datasets.
  arXiv  Detail & Related papers  (2024-08-13T07:27:02Z)
• An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms [0.0]
  Using face masks have become crucial in our daily life with the recent world-wide COVID-19 pandemic. The effect of mask-wearing in face recognition is yet an understudied issue. We use six conventional machine learning algorithms, which are SVC, KNN, LDA, DT, LR and NB, to find out the ones which perform best.
  arXiv  Detail & Related papers  (2023-06-14T14:50:23Z)
• MaskSearch: Querying Image Masks at Scale [60.82746984506577]
  MaskSearch is a system that focuses on accelerating queries over databases of image masks while guaranteeing the correctness of query results. Experiments with our prototype show that MaskSearch, using indexes approximately 5% of the compressed data size, accelerates individual queries by up to two orders of magnitude.
  arXiv  Detail & Related papers  (2023-05-03T18:28:14Z)
• Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations [86.47908754383198]
  Open-Vocabulary (OV) methods leverage large-scale image-caption pairs and vision-language models to learn novel categories. Our method generates pseudo-mask annotations by leveraging the localization ability of a pre-trained vision-language model for objects present in image-caption pairs. Our method trained with just pseudo-masks significantly improves the mAP scores on the MS-COCO dataset and OpenImages dataset.
  arXiv  Detail & Related papers  (2023-03-29T17:58:39Z)
• Improving Masked Autoencoders by Learning Where to Mask [65.89510231743692]
  Masked image modeling is a promising self-supervised learning method for visual data. We present AutoMAE, a framework that uses Gumbel-Softmax to interlink an adversarially-trained mask generator and a mask-guided image modeling process. In our experiments, AutoMAE is shown to provide effective pretraining models on standard self-supervised benchmarks and downstream tasks.
  arXiv  Detail & Related papers  (2023-03-12T05:28:55Z)
• What to Hide from Your Students: Attention-Guided Masked Image Modeling [32.402567373491834]
  We argue that image token masking is fundamentally different from token masking in text. We introduce a novel masking strategy, called attention-guided masking (AttMask)
  arXiv  Detail & Related papers  (2022-03-23T20:52:50Z)
• MaskMTL: Attribute prediction in masked facial images with deep multitask learning [9.91045425400833]
  This paper presents a deep Multi-Task Learning (MTL) approach to jointly estimate various heterogeneous attributes from a single masked facial image. The proposed approach supersedes in performance to other competing techniques.
  arXiv  Detail & Related papers  (2022-01-09T13:03:29Z)
• Mask-invariant Face Recognition through Template-level Knowledge Distillation [3.727773051465455]
  Masks affect the performance of previous face recognition systems. We propose a mask-invariant face recognition solution (MaskInv) In addition to the distilled knowledge, the student network benefits from additional guidance by margin-based identity classification loss.
  arXiv  Detail & Related papers  (2021-12-10T16:19:28Z)
• Self-Supervised Visual Representations Learning by Contrastive Mask Prediction [129.25459808288025]
  We propose a novel contrastive mask prediction (CMP) task for visual representation learning. MaskCo contrasts region-level features instead of view-level features, which makes it possible to identify the positive sample without any assumptions. We evaluate MaskCo on training datasets beyond ImageNet and compare its performance with MoCo V2.
  arXiv  Detail & Related papers  (2021-08-18T02:50:33Z)
• Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection [103.7264459186552]
  Face presentation attack detection (PAD) is essential to secure face recognition systems. Most existing 3D mask PAD benchmarks suffer from several drawbacks. We introduce a largescale High-Fidelity Mask dataset to bridge the gap to real-world applications.
  arXiv  Detail & Related papers  (2021-04-13T12:48:38Z)

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.