Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation

• URL: http://arxiv.org/abs/2005.06831v1
• Date: Thu, 14 May 2020 09:21:14 GMT
• Title: Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation
• Abstract summary: We present a novel pipeline for semantic segmenation that detects out-of-distribution (OOD) segments by means of the deep neural network's prediction. In our experiments we demonstrate that the deployed OOD approach is suitable for detecting out-of-distribution concepts.
• Score: 9.496524884855559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When deploying deep learning technology in self-driving cars, deep neural networks are constantly exposed to domain shifts. These include, e.g., changes in weather conditions, time of day, and long-term temporal shift. In this work we utilize a deep neural network trained on the Cityscapes dataset containing urban street scenes and infer images from a different dataset, the A2D2 dataset, containing also countryside and highway images. We present a novel pipeline for semantic segmenation that detects out-of-distribution (OOD) segments by means of the deep neural network's prediction and performs image retrieval after feature extraction and dimensionality reduction on image patches. In our experiments we demonstrate that the deployed OOD approach is suitable for detecting out-of-distribution concepts. Furthermore, we evaluate the image patch retrieval qualitatively as well as quantitatively by means of the semi-compatible A2D2 ground truth and obtain mAP values of up to 52.2%.

Related papers

• UAVs and Neural Networks for search and rescue missions [0.0]
  We present a method for detecting objects of interest, including cars, humans, and fire, in aerial images captured by unmanned aerial vehicles (UAVs) To achieve this, we use artificial neural networks and create a dataset for supervised learning.
  arXiv  Detail & Related papers  (2023-10-09T08:27:35Z)
• Dsfer-Net: A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Networks [35.415260892693745]
  We propose a Deep Supervision and FEature Retrieval network (Dsfer-Net) for bitemporal change detection. Specifically, the highly representative deep features of bitemporal images are jointly extracted through a fully convolutional Siamese network. Our end-to-end network establishes a novel framework by aggregating retrieved features and feature pairs from different layers.
  arXiv  Detail & Related papers  (2023-04-03T16:01:03Z)
• An Empirical Study of Remote Sensing Pretraining [117.90699699469639]
  We conduct an empirical study of remote sensing pretraining (RSP) on aerial images. RSP can help deliver distinctive performances in scene recognition tasks. RSP mitigates the data discrepancies of traditional ImageNet pretraining on RS images, but it may still suffer from task discrepancies.
  arXiv  Detail & Related papers  (2022-04-06T13:38:11Z)
• Toward Data-Driven STAP Radar [23.333816677794115]
  We characterize our data-driven approach to space-time adaptive processing (STAP) radar. We generate a rich example dataset of received radar signals by randomly placing targets of variable strengths in a predetermined region. For each data sample within this region, we generate heatmap tensors in range, azimuth, and elevation of the output power of a beamformer. In an airborne scenario, the moving radar creates a sequence of these time-indexed image stacks, resembling a video.
  arXiv  Detail & Related papers  (2022-01-26T02:28:13Z)
• Residual Moment Loss for Medical Image Segmentation [56.72261489147506]
  Location information is proven to benefit the deep learning models on capturing the manifold structure of target objects. Most existing methods encode the location information in an implicit way, for the network to learn. We propose a novel loss function, namely residual moment (RM) loss, to explicitly embed the location information of segmentation targets.
  arXiv  Detail & Related papers  (2021-06-27T09:31:49Z)
• Hierarchical Convolutional Neural Network with Feature Preservation and Autotuned Thresholding for Crack Detection [5.735035463793008]
  Drone imagery is increasingly used in automated inspection for infrastructure surface defects. This paper proposes a deep learning approach using hierarchical convolutional neural networks with feature preservation. The proposed technique is then applied to identify surface cracks on the surface of roads, bridges or pavements.
  arXiv  Detail & Related papers  (2021-04-21T13:07:58Z)
• Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion [56.85837052421469]
  Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars. In this paper, we study the problem of predicting dense depth from a single RGB image with optional sparse measurements from low-cost active depth sensors. We introduce Sparse Networks (SANs), a new module enabling monodepth networks to perform both the tasks of depth prediction and completion.
  arXiv  Detail & Related papers  (2021-03-30T21:22:26Z)
• SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images [94.36401543589523]
  We introduce the concept of semantic objectness to exploit the geometric relationship of these two tasks. We then propose a Semantic Object and Depth Estimation Network (SOSD-Net) based on the objectness assumption. To the best of our knowledge, SOSD-Net is the first network that exploits the geometry constraint for simultaneous monocular depth estimation and semantic segmentation.
  arXiv  Detail & Related papers  (2021-01-19T02:41:03Z)
• Ventral-Dorsal Neural Networks: Object Detection via Selective Attention [51.79577908317031]
  We propose a new framework called Ventral-Dorsal Networks (VDNets) Inspired by the structure of the human visual system, we propose the integration of a "Ventral Network" and a "Dorsal Network" Our experimental results reveal that the proposed method outperforms state-of-the-art object detection approaches.
  arXiv  Detail & Related papers  (2020-05-15T23:57:36Z)
• Saliency Enhancement using Gradient Domain Edges Merging [65.90255950853674]
  We develop a method to merge the edges with the saliency maps to improve the performance of the saliency. This leads to our proposed saliency enhancement using edges (SEE) with an average improvement of at least 3.4 times higher on the DUT-OMRON dataset. The SEE algorithm is split into 2 parts, SEE-Pre for preprocessing and SEE-Post pour postprocessing.
  arXiv  Detail & Related papers  (2020-02-11T14:04:56Z)

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.