3SD: Self-Supervised Saliency Detection With No Labels

• URL: http://arxiv.org/abs/2203.04478v1
• Date: Wed, 9 Mar 2022 01:40:28 GMT
• Title: 3SD: Self-Supervised Saliency Detection With No Labels
• Authors: Rajeev Yasarla, Renliang Weng, Wongun Choi, Vishal Patel, and Amir Sadeghian
• Abstract summary: We present a conceptually simple self-supervised method for saliency detection. Our method generates and uses pseudo-ground truth labels for training.
• Score: 19.260185488168982
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a conceptually simple self-supervised method for saliency detection. Our method generates and uses pseudo-ground truth labels for training. The generated pseudo-GT labels don't require any kind of human annotations (e.g., pixel-wise labels or weak labels like scribbles). Recent works show that features extracted from classification tasks provide important saliency cues like structure and semantic information of salient objects in the image. Our method, called 3SD, exploits this idea by adding a branch for a self-supervised classification task in parallel with salient object detection, to obtain class activation maps (CAM maps). These CAM maps along with the edges of the input image are used to generate the pseudo-GT saliency maps to train our 3SD network. Specifically, we propose a contrastive learning-based training on multiple image patches for the classification task. We show the multi-patch classification with contrastive loss improves the quality of the CAM maps compared to naive classification on the entire image. Experiments on six benchmark datasets demonstrate that without any labels, our 3SD method outperforms all existing weakly supervised and unsupervised methods, and its performance is on par with the fully-supervised methods. Code is available at :https://github.com/rajeevyasarla/3SD

Related papers

• You Only Need One Thing One Click: Self-Training for Weakly Supervised 3D Scene Understanding [107.06117227661204]
  We propose One Thing One Click'', meaning that the annotator only needs to label one point per object. We iteratively conduct the training and label propagation, facilitated by a graph propagation module. Our model can be compatible to 3D instance segmentation equipped with a point-clustering strategy.
  arXiv  Detail & Related papers  (2023-03-26T13:57:00Z)
• Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning [59.64695628433855]
  We propose a novel cross-modality weakly supervised method for 3D segmentation, incorporating complementary information from unlabeled images. Basically, we design a dual-branch network equipped with an active labeling strategy, to maximize the power of tiny parts of labels. Our method even outperforms the state-of-the-art fully supervised competitors with less than 1% actively selected annotations.
  arXiv  Detail & Related papers  (2022-09-16T07:59:04Z)
• An Empirical Study of Pseudo-Labeling for Image-based 3D Object Detection [72.30883544352918]
  We investigate whether pseudo-labels can provide effective supervision for the baseline models under varying settings. We achieve 20.23 AP for moderate level on the KITTI-3D testing set without bells and whistles, improving the baseline model by 6.03 AP. We hope this work can provide insights for the image-based 3D detection community under a semi-supervised setting.
  arXiv  Detail & Related papers  (2022-08-15T12:17:46Z)
• Mixed Supervision Learning for Whole Slide Image Classification [88.31842052998319]
  We propose a mixed supervision learning framework for super high-resolution images. During the patch training stage, this framework can make use of coarse image-level labels to refine self-supervised learning. A comprehensive strategy is proposed to suppress pixel-level false positives and false negatives.
  arXiv  Detail & Related papers  (2021-07-02T09:46:06Z)
• One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation [78.36781565047656]
  We propose "One Thing One Click," meaning that the annotator only needs to label one point per object. We iteratively conduct the training and label propagation, facilitated by a graph propagation module. Our results are also comparable to those of the fully supervised counterparts.
  arXiv  Detail & Related papers  (2021-04-06T02:27:25Z)
• Weakly-Supervised Image Semantic Segmentation Using Graph Convolutional Networks [9.066817971329899]
  weakly-supervised image semantic segmentation based on image-level class labels. One common approach to this task is to propagate the activation scores of Class Activation Maps (CAMs) using a random-walk mechanism. We propose a Graph Convolutional Network (GCN)-based feature propagation framework.
  arXiv  Detail & Related papers  (2021-03-31T02:05:01Z)
• Grafit: Learning fine-grained image representations with coarse labels [114.17782143848315]
  This paper tackles the problem of learning a finer representation than the one provided by training labels. By jointly leveraging the coarse labels and the underlying fine-grained latent space, it significantly improves the accuracy of category-level retrieval methods.
  arXiv  Detail & Related papers  (2020-11-25T19:06:26Z)
• Unsupervised Self-training Algorithm Based on Deep Learning for Optical Aerial Images Change Detection [17.232244800511523]
  We present a novel unsupervised self-training algorithm (USTA) for optical aerial images change detection. The whole process of the algorithm is an unsupervised process without manually marked labels. Experimental results on the real datasets demonstrate competitive performance of our proposed method.
  arXiv  Detail & Related papers  (2020-10-15T01:51:46Z)

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.