Feature-Driven Super-Resolution for Object Detection

• URL: http://arxiv.org/abs/2004.00554v1
• Date: Wed, 1 Apr 2020 16:33:07 GMT
• Title: Feature-Driven Super-Resolution for Object Detection
• Authors: Bin Wang, Tao Lu, Yanduo Zhang
• Abstract summary: This paper proposes a simple but powerful feature-driven super-resolution (FDSR) to improve the detection performance of low-resolution (LR) images. FDSR outperforms the detection performance mAP on MS COCO validation, VOC2007 databases with good generalization to other detection networks.
• Score: 13.748941620767452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although some convolutional neural networks (CNNs) based super-resolution (SR) algorithms yield good visual performances on single images recently. Most of them focus on perfect perceptual quality but ignore specific needs of subsequent detection task. This paper proposes a simple but powerful feature-driven super-resolution (FDSR) to improve the detection performance of low-resolution (LR) images. First, the proposed method uses feature-domain prior which extracts from an existing detector backbone to guide the HR image reconstruction. Then, with the aligned features, FDSR update SR parameters for better detection performance. Comparing with some state-of-the-art SR algorithms with 4$\times$ scale factor, FDSR outperforms the detection performance mAP on MS COCO validation, VOC2007 databases with good generalization to other detection networks.

Related papers

• Improving Neural Surface Reconstruction with Feature Priors from Multi-View Image [87.00660347447494]
  Recent advancements in Neural Surface Reconstruction (NSR) have significantly improved multi-view reconstruction when coupled with volume rendering. We propose an investigation into feature-level consistent loss, aiming to harness valuable feature priors from diverse pretext visual tasks. Our results, analyzed on DTU and EPFL, reveal that feature priors from image matching and multi-view stereo datasets outperform other pretext tasks.
  arXiv  Detail & Related papers  (2024-08-04T16:09:46Z)
• Sewer Image Super-Resolution with Depth Priors and Its Lightweight Network [11.13549330516683]
  Quick-view (QV) technique serves as a primary method for detecting defects within sewerage systems. Super-resolution is an effective way to improve image quality and has been applied in a variety of scenes. This study introduces a novel Depth-guided, Reference-based Super-Resolution framework denoted as DSRNet.
  arXiv  Detail & Related papers  (2024-07-27T14:45:34Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Heuristic Hyperparameter Choice for Image Anomaly Detection [0.3867363075280543]
  Anomaly detection in images is a fundamental computer vision problem by deep learning neural network. Models are usually pretrained on a large dataset for classification tasks such as ImageNet. We aim to do the dimension reduction of Negated Principal Component Analysis (NPCA) for these features.
  arXiv  Detail & Related papers  (2023-07-20T19:20:35Z)
• CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution [158.2282163651066]
  This paper proposes a continuous implicit attention-in-attention network, called CiaoSR. We explicitly design an implicit attention network to learn the ensemble weights for the nearby local features. We embed a scale-aware attention in this implicit attention network to exploit additional non-local information.
  arXiv  Detail & Related papers  (2022-12-08T15:57:46Z)
• Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection [77.3530907443279]
  We propose a novel self-supervised framework to detect objects in degraded low resolution images. Our methods has achieved superior performance compared with existing methods when facing variant degradation situations.
  arXiv  Detail & Related papers  (2022-08-05T09:36:13Z)
• Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution [64.15915577164894]
  A hierarchical image super-resolution network (HSRNet) is proposed to suppress the influence of aliasing. HSRNet achieves better quantitative and visual performance than other works, and remits the aliasing more effectively.
  arXiv  Detail & Related papers  (2022-06-07T14:55:32Z)
• Asymmetric CNN for image super-resolution [102.96131810686231]
  Deep convolutional neural networks (CNNs) have been widely applied for low-level vision over the past five years. We propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a mem?ory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution. Our ACNet can effectively address single image super-resolution (SISR), blind SISR and blind SISR of blind noise problems.
  arXiv  Detail & Related papers  (2021-03-25T07:10:46Z)
• CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images [0.9462808515258465]
  In this paper, we discuss the role of discriminative features in object detection. We then propose a Critical Feature Capturing Network (CFC-Net) to improve detection accuracy. We show that our method achieves superior detection performance compared with many state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-01-18T02:31:09Z)
• Learning-Based Quality Assessment for Image Super-Resolution [25.76907513611358]
  We build a large-scale SR image database using a novel semi-automatic labeling approach. The resulting SR Image quality database contains 8,400 images of 100 natural scenes. We train an end-to-end Deep Image SR Quality (DISQ) model by employing two-stream Deep Neural Networks (DNNs) for feature extraction, followed by a feature fusion network for quality prediction.
  arXiv  Detail & Related papers  (2020-12-16T04:06:27Z)
• Deep Iterative Residual Convolutional Network for Single Image Super-Resolution [31.934084942626257]
  We propose a deep Iterative Super-Resolution Residual Convolutional Network (ISRResCNet) It exploits the powerful image regularization and large-scale optimization techniques by training the deep network in an iterative manner with a residual learning approach. Our method with a few trainable parameters improves the results for different scaling factors in comparison with the state-of-art methods.
  arXiv  Detail & Related papers  (2020-09-07T12:54:14Z)

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.