Large-Scale Unsupervised Object Discovery

• URL: http://arxiv.org/abs/2106.06650v1
• Date: Sat, 12 Jun 2021 00:29:49 GMT
• Title: Large-Scale Unsupervised Object Discovery
• Authors: Huy V. Vo, Elena Sizikova, Cordelia Schmid, Patrick P\'erez, Jean Ponce
• Abstract summary: unsupervised object discovery (UOD) do not scale up to large datasets without approximations which compromise their performance. We propose a novel formulation of UOD as a ranking problem, amenable to the arsenal of distributed methods available for eigenvalue problems and link analysis.
• Score: 80.60458324771571
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing approaches to unsupervised object discovery (UOD) do not scale up to large datasets without approximations which compromise their performance. We propose a novel formulation of UOD as a ranking problem, amenable to the arsenal of distributed methods available for eigenvalue problems and link analysis. Extensive experiments with COCO and OpenImages demonstrate that, in the single-object discovery setting where a single prominent object is sought in each image, the proposed LOD (Large-scale Object Discovery) approach is on par with, or better than the state of the art for medium-scale datasets (up to 120K images), and over 37% better than the only other algorithms capable of scaling up to 1.7M images. In the multi-object discovery setting where multiple objects are sought in each image, the proposed LOD is over 14% better in average precision (AP) than all other methods for datasets ranging from 20K to 1.7M images.

Related papers

• SOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detection [59.868772767818975]
  We propose a simple yet effective Semi-supervised Oriented Object Detection method termed SOOD++. Specifically, we observe that objects from aerial images are usually arbitrary orientations, small scales, and aggregation. Extensive experiments conducted on various multi-oriented object datasets under various labeled settings demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-07-01T07:03:51Z)
• Retrieval Robust to Object Motion Blur [54.34823913494456]
  We propose a method for object retrieval in images that are affected by motion blur. We present the first large-scale datasets for blurred object retrieval. Our method outperforms state-of-the-art retrieval methods on the new blur-retrieval datasets.
  arXiv  Detail & Related papers  (2024-04-27T23:22:39Z)
• YOLC: You Only Look Clusters for Tiny Object Detection in Aerial Images [33.80392696735718]
  YOLC (You Only Look Clusters) is an efficient and effective framework that builds on an anchor-free object detector, CenterNet. To overcome the challenges posed by large-scale images and non-uniform object distribution, we introduce a Local Scale Module (LSM) that adaptively searches cluster regions for zooming in for accurate detection. We perform extensive experiments on two aerial image datasets, including Visdrone 2019 and UAVDT, to demonstrate the effectiveness and superiority of our proposed approach.
  arXiv  Detail & Related papers  (2024-04-09T10:03:44Z)
• SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection [79.23689506129733]
  We establish a new benchmark dataset and an open-source method for large-scale SAR object detection. Our dataset, SARDet-100K, is a result of intense surveying, collecting, and standardizing 10 existing SAR detection datasets. To the best of our knowledge, SARDet-100K is the first COCO-level large-scale multi-class SAR object detection dataset ever created.
  arXiv  Detail & Related papers  (2024-03-11T09:20:40Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art [7.74389937337756]
  This paper reviews current datasets and object detection methods (deep learning-based) for remote sensing images. We propose a large-scale, publicly available benchmark Remote Sensing Super-resolution Object Detection dataset. We also propose a novel Multi-class Cyclic super-resolution Generative adversarial network with Residual feature aggregation (MCGR) and auxiliary YOLOv5 detector to benchmark image super-resolution-based object detection.
  arXiv  Detail & Related papers  (2021-11-05T04:56:34Z)
• FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery [21.9319970004788]
  We propose a novel benchmark dataset with more than 1 million instances and more than 15,000 images for Fine-grAined object recognItion in high-Resolution remote sensing imagery. All objects in the FAIR1M dataset are annotated with respect to 5 categories and 37 sub-categories by oriented bounding boxes.
  arXiv  Detail & Related papers  (2021-03-09T17:20:15Z)
• Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges [124.48654341780431]
  We present a large-scale dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. We build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated.
  arXiv  Detail & Related papers  (2021-02-24T11:20:55Z)
• Toward unsupervised, multi-object discovery in large-scale image collections [26.39475298878971]
  This paper builds on the optimization approach of Vo et al. We propose a novel saliency-based region proposal algorithm. We exploit the inherent hierarchical structure of proposals as an effective regularizer.
  arXiv  Detail & Related papers  (2020-07-06T11:43:47Z)

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.