Sparse Generation: Making Pseudo Labels Sparse for weakly supervision with points

• URL: http://arxiv.org/abs/2403.19306v1
• Date: Thu, 28 Mar 2024 10:42:49 GMT
• Title: Sparse Generation: Making Pseudo Labels Sparse for weakly supervision with points
• Authors: Tian Ma, Chuyang Shang, Wanzhu Ren, Yuancheng Li, Jiiayi Yang, Jiali Qian,
• Abstract summary: We consider the generation of weakly supervised pseudo labels as the result of model's sparse output. We propose a method called Sparse Generation to make pseudo labels sparse.
• Score: 2.2241974678268903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, research on point weakly supervised object detection (PWSOD) methods in the field of computer vision has attracted people's attention. However, existing pseudo labels generation methods perform poorly in a small amount of supervised annotation data and dense object detection tasks. We consider the generation of weakly supervised pseudo labels as the result of model's sparse output, and propose a method called Sparse Generation to make pseudo labels sparse. It constructs dense tensors through the relationship between data and detector model, optimizes three of its parameters, and obtains a sparse tensor via coordinated calculation, thereby indirectly obtaining higher quality pseudo labels, and solving the model's density problem in the situation of only a small amount of supervised annotation data can be used. On two broadly used open-source datasets (RSOD, SIMD) and a self-built dataset (Bullet-Hole), the experimental results showed that the proposed method has a significant advantage in terms of overall performance metrics, comparing to that state-of-the-art method.

Related papers

• TrajSSL: Trajectory-Enhanced Semi-Supervised 3D Object Detection [59.498894868956306]
  Pseudo-labeling approaches to semi-supervised learning adopt a teacher-student framework. We leverage pre-trained motion-forecasting models to generate object trajectories on pseudo-labeled data. Our approach improves pseudo-label quality in two distinct manners.
  arXiv  Detail & Related papers  (2024-09-17T05:35:00Z)
• Dual-Perspective Knowledge Enrichment for Semi-Supervised 3D Object Detection [55.210991151015534]
  We present a novel Dual-Perspective Knowledge Enrichment approach named DPKE for semi-supervised 3D object detection. Our DPKE enriches the knowledge of limited training data, particularly unlabeled data, from two perspectives: data-perspective and feature-perspective.
  arXiv  Detail & Related papers  (2024-01-10T08:56:07Z)
• All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation [67.30502812804271]
  Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where only sparse ground-truth labels are available for learning. We propose a novel learning strategy to regularize the generated pseudo-labels and effectively narrow the gaps between pseudo-labels and model predictions.
  arXiv  Detail & Related papers  (2023-05-25T08:19:31Z)
• Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point Supervision [63.429704654271475]
  We propose a novel weakly supervised method RWSeg that only requires labeling one object with one point. With these sparse weak labels, we introduce a unified framework with two branches to propagate semantic and instance information. Specifically, we propose a Cross-graph Competing Random Walks (CRW) algorithm that encourages competition among different instance graphs.
  arXiv  Detail & Related papers  (2022-08-10T02:14:39Z)
• Weakly-Supervised Salient Object Detection Using Point Supervison [17.88596733603456]
  Current state-of-the-art saliency detection models rely heavily on large datasets of accurate pixel-wise annotations. We propose a novel weakly-supervised salient object detection method using point supervision. Our method outperforms the previous state-of-the-art methods trained with the stronger supervision.
  arXiv  Detail & Related papers  (2022-03-22T12:16:05Z)
• Semi-supervised Salient Object Detection with Effective Confidence Estimation [35.0990691497574]
  We study semi-supervised salient object detection with access to a small number of labeled samples and a large number of unlabeled samples. We model the nature of human saliency labels using the latent variable of the Conditional Energy-based Model. With only 1/16 labeled samples, our model achieves competitive performance compared with state-of-the-art fully-supervised models.
  arXiv  Detail & Related papers  (2021-12-28T07:14:48Z)
• WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection [75.80075054706079]
  We propose a weakly- and semi-supervised object detection framework (WSSOD) An agent detector is first trained on a joint dataset and then used to predict pseudo bounding boxes on weakly-annotated images. The proposed framework demonstrates remarkable performance on PASCAL-VOC and MSCOCO benchmark, achieving a high performance comparable to those obtained in fully-supervised settings.
  arXiv  Detail & Related papers  (2021-05-21T11:58:50Z)
• Weakly-Supervised Salient Object Detection via Scribble Annotations [54.40518383782725]
  We propose a weakly-supervised salient object detection model to learn saliency from scribble labels. We present a new metric, termed saliency structure measure, to measure the structure alignment of the predicted saliency maps. Our method not only outperforms existing weakly-supervised/unsupervised methods, but also is on par with several fully-supervised state-of-the-art models.
  arXiv  Detail & Related papers  (2020-03-17T12:59:50Z)

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.