Robust Object Detection under Occlusion with Context-Aware CompositionalNets

• URL: http://arxiv.org/abs/2005.11643v2
• Date: Sat, 30 May 2020 14:33:14 GMT
• Title: Robust Object Detection under Occlusion with Context-Aware CompositionalNets
• Authors: Angtian Wang, Yihong Sun, Adam Kortylewski, Alan Yuille
• Abstract summary: Compositional convolutional neural networks (CompositionalNets) have been shown to be robust at classifying occluded objects. We propose to overcome two limitations of CompositionalNets which will enable them to detect partially occluded objects.
• Score: 21.303976151518125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting partially occluded objects is a difficult task. Our experimental results show that deep learning approaches, such as Faster R-CNN, are not robust at object detection under occlusion. Compositional convolutional neural networks (CompositionalNets) have been shown to be robust at classifying occluded objects by explicitly representing the object as a composition of parts. In this work, we propose to overcome two limitations of CompositionalNets which will enable them to detect partially occluded objects: 1) CompositionalNets, as well as other DCNN architectures, do not explicitly separate the representation of the context from the object itself. Under strong object occlusion, the influence of the context is amplified which can have severe negative effects for detection at test time. In order to overcome this, we propose to segment the context during training via bounding box annotations. We then use the segmentation to learn a context-aware CompositionalNet that disentangles the representation of the context and the object. 2) We extend the part-based voting scheme in CompositionalNets to vote for the corners of the object's bounding box, which enables the model to reliably estimate bounding boxes for partially occluded objects. Our extensive experiments show that our proposed model can detect objects robustly, increasing the detection performance of strongly occluded vehicles from PASCAL3D+ and MS-COCO by 41% and 35% respectively in absolute performance relative to Faster R-CNN.

Related papers

• Object-Centric Multiple Object Tracking [124.30650395969126]
  This paper proposes a video object-centric model for multiple-object tracking pipelines. It consists of an index-merge module that adapts the object-centric slots into detection outputs and an object memory module. Benefited from object-centric learning, we only require sparse detection labels for object localization and feature binding.
  arXiv  Detail & Related papers  (2023-09-01T03:34:12Z)
• 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)
• DQnet: Cross-Model Detail Querying for Camouflaged Object Detection [54.82390534024954]
  A convolutional neural network (CNN) for camouflaged object detection tends to activate local discriminative regions while ignoring complete object extent. In this paper, we argue that partial activation is caused by the intrinsic characteristics of CNN. In order to obtain feature maps that could activate full object extent, a novel framework termed Cross-Model Detail Querying network (DQnet) is proposed.
  arXiv  Detail & Related papers  (2022-12-16T06:23:58Z)
• A Tri-Layer Plugin to Improve Occluded Detection [100.99802831241583]
  We propose a simple '' module for the detection head of two-stage object detectors to improve the recall of partially occluded objects. The module predicts a tri-layer of segmentation masks for the target object, the occluder and the occludee, and by doing so is able to better predict the mask of the target object. We also establish a COCO evaluation dataset to measure the recall performance of partially occluded and separated objects.
  arXiv  Detail & Related papers  (2022-10-18T17:59:51Z)
• ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers [73.29057814695459]
  ComplETR is designed to explicitly complete missing annotations in partially annotated dense scene datasets. This reduces the need to annotate every object instance in the scene thereby reducing annotation cost. We show performance improvement for several popular detectors such as Faster R-CNN, Cascade R-CNN, CenterNet2, and Deformable DETR.
  arXiv  Detail & Related papers  (2022-09-13T00:11:16Z)
• VIN: Voxel-based Implicit Network for Joint 3D Object Detection and Segmentation for Lidars [12.343333815270402]
  A unified neural network structure is presented for joint 3D object detection and point cloud segmentation. We leverage rich supervision from both detection and segmentation labels rather than using just one of them.
  arXiv  Detail & Related papers  (2021-07-07T02:16:20Z)
• Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [72.38919601150175]
  We propose Bilayer Convolutional Network (BCNet) to segment highly-overlapping objects. BCNet detects the occluding objects (occluder) and the bottom GCN layer infers partially occluded instance (occludee)
  arXiv  Detail & Related papers  (2021-03-23T06:25:42Z)
• Robust Instance Segmentation through Reasoning about Multi-Object Occlusion [9.536947328412198]
  We propose a deep network for multi-object instance segmentation that is robust to occlusion. Our work builds on Compositional Networks, which learn a generative model of neural feature activations to locate occluders. In particular, we obtain feed-forward predictions of the object classes and their instance and occluder segmentations.
  arXiv  Detail & Related papers  (2020-12-03T17:41:55Z)
• Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion [21.737411464598797]
  We show that black-box deep convolutional neural networks (DCNNs) have only limited robustness to partial occlusion. We overcome these limitations by unifying DCNNs with part-based models into Compositional Convolutional Neural Networks (CompositionalNets) Our experiments show that CompositionalNets improve by a large margin over their non-compositional counterparts at classifying and detecting partially occluded objects.
  arXiv  Detail & Related papers  (2020-06-28T08:18:19Z)
• Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion [18.276428975330813]
  Recent findings show that deep convolutional neural networks (DCNNs) do not generalize well under partial occlusion. Inspired by the success of compositional models at classifying partially occluded objects, we propose to integrate compositional models and DCNNs into a unified deep model. We conduct classification experiments on artificially occluded images as well as real images of partially occluded objects from the MS-COCO dataset. Our proposed model outperforms standard DCNNs by a large margin at classifying partially occluded objects, even when it has not been exposed to occluded objects during training.
  arXiv  Detail & Related papers  (2020-03-10T01:45:38Z)

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.