FSD: Fully-Specialized Detector via Neural Architecture Search

• URL: http://arxiv.org/abs/2305.16649v4
• Date: Fri, 21 Jul 2023 05:46:30 GMT
• Title: FSD: Fully-Specialized Detector via Neural Architecture Search
• Authors: Zhe Huang and Yudian Li
• Abstract summary: We first propose and examine a fully-automatic pipeline to design a fully-specialized detector (FSD) On the DeepLesion dataset, extensive results show that FSD can achieve 3.1 mAP gain while using approximately 40% fewer parameters on binary lesion detection task.
• Score: 2.149718433100702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most generic object detectors are mainly built for standard object detection tasks such as COCO and PASCAL VOC. They might not work well and/or efficiently on tasks of other domains consisting of images that are visually different from standard datasets. To this end, many advances have been focused on adapting a general-purposed object detector with limited domain-specific designs. However, designing a successful task-specific detector requires extraneous manual experiments and parameter tuning through trial and error. In this paper, we first propose and examine a fully-automatic pipeline to design a fully-specialized detector (FSD) which mainly incorporates a neural-architectural-searched model by exploring ideal network structures over the backbone and task-specific head. On the DeepLesion dataset, extensive results show that FSD can achieve 3.1 mAP gain while using approximately 40% fewer parameters on binary lesion detection task and improved the mAP by around 10% on multi-type lesion detection task via our region-aware graph modeling compared with existing general-purposed medical lesion detection networks.

Related papers

• Renormalized Connection for Scale-preferred Object Detection in Satellite Imagery [51.83786195178233]
  We design a Knowledge Discovery Network (KDN) to implement the renormalization group theory in terms of efficient feature extraction. Renormalized connection (RC) on the KDN enables synergistic focusing'' of multi-scale features. RCs extend the multi-level feature's divide-and-conquer'' mechanism of the FPN-based detectors to a wide range of scale-preferred tasks.
  arXiv  Detail & Related papers  (2024-09-09T13:56:22Z)
• Efficient Meta-Learning Enabled Lightweight Multiscale Few-Shot Object Detection in Remote Sensing Images [15.12889076965307]
  YOLOv7 one-stage detector is subjected to a novel meta-learning training framework. This transformation allows the detector to adeptly address FSOD tasks while capitalizing on its inherent advantage of lightweight. To validate the effectiveness of our proposed detector, we conducted performance comparisons with current state-of-the-art detectors.
  arXiv  Detail & Related papers  (2024-04-29T04:56:52Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• Minimal-Configuration Anomaly Detection for IIoT Sensors [0.2462953128215087]
  Low-cost IoT sensor platforms in industry boost the demand for anomaly detection solutions. Recent advances in deep learning offer promising methods for detecting anomalies in sensor data recordings. We consider this work as being the first step towards a generic anomaly detection method.
  arXiv  Detail & Related papers  (2021-10-08T11:52:52Z)
• Finding Facial Forgery Artifacts with Parts-Based Detectors [73.08584805913813]
  We design a series of forgery detection systems that each focus on one individual part of the face. We use these detectors to perform detailed empirical analysis on the FaceForensics++, Celeb-DF, and Facebook Deepfake Detection Challenge datasets.
  arXiv  Detail & Related papers  (2021-09-21T16:18:45Z)
• One-Shot Object Affordance Detection in the Wild [76.46484684007706]
  Affordance detection refers to identifying the potential action possibilities of objects in an image. We devise a One-Shot Affordance Detection Network (OSAD-Net) that estimates the human action purpose and then transfers it to help detect the common affordance from all candidate images. With complex scenes and rich annotations, our PADv2 dataset can be used as a test bed to benchmark affordance detection methods.
  arXiv  Detail & Related papers  (2021-08-08T14:53:10Z)
• Small and large scale critical infrastructures detection based on deep learning using high resolution orthogonal images [0.0]
  This paper presents a smart dataset as well as a resolution-independent critical infrastructure detection system. In particular, guided by the performance of the detection model, we built a dataset organized into two scales, small and large scale. DetDSCI methodology achieves up to 37,53% F1 improvement with respect to the baseline detector.
  arXiv  Detail & Related papers  (2021-05-25T11:38:15Z)
• A DCNN-based Arbitrarily-Oriented Object Detector for Quality Control and Inspection Application [10.076629346147639]
  A lightweight neural network is exploited to achieve oriented detection results using a regression method. The first stage of the proposed method is capable of detecting the small targets considered in the two scenarios. In the second stage, despite the simplicity, it is efficient to detect elongated targets while maintaining high running efficiency.
  arXiv  Detail & Related papers  (2021-01-19T00:23:27Z)
• MRDet: A Multi-Head Network for Accurate Oriented Object Detection in Aerial Images [51.227489316673484]
  We propose an arbitrary-oriented region proposal network (AO-RPN) to generate oriented proposals transformed from horizontal anchors. To obtain accurate bounding boxes, we decouple the detection task into multiple subtasks and propose a multi-head network. Each head is specially designed to learn the features optimal for the corresponding task, which allows our network to detect objects accurately.
  arXiv  Detail & Related papers  (2020-12-24T06:36:48Z)
• Representation Sharing for Fast Object Detector Search and Beyond [38.18583590914755]
  We propose Fast And Diverse (FAD) to better explore the optimal configuration of receptive fields and convolution types in the sub-networks for one-stage detectors. FAD achieves prominent improvements on two types of one-stage detectors with various backbones.
  arXiv  Detail & Related papers  (2020-07-23T15:39:44Z)
• NADS: Neural Architecture Distribution Search for Uncertainty Awareness [79.18710225716791]
  Machine learning (ML) systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a distribution different from training data. Existing OoD detection approaches are prone to errors and even sometimes assign higher likelihoods to OoD samples. We propose Neural Architecture Distribution Search (NADS) to identify common building blocks among all uncertainty-aware architectures.
  arXiv  Detail & Related papers  (2020-06-11T17:39:07Z)

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.