Object Detection Based on Distributed Convolutional Neural Networks

• URL: http://arxiv.org/abs/2603.28050v1
• Date: Mon, 30 Mar 2026 05:30:45 GMT
• Title: Object Detection Based on Distributed Convolutional Neural Networks
• Abstract summary: Based on the Distributed Convolutional Neural Network(DisCNN), a straightforward object detection method is proposed.<n>The modules of the output vector of a DisCNN with respect to a specific positive class are positively monotonic with the presence probabilities of the positive features.
• Score: 5.912314032588749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Based on the Distributed Convolutional Neural Network(DisCNN), a straightforward object detection method is proposed. The modules of the output vector of a DisCNN with respect to a specific positive class are positively monotonic with the presence probabilities of the positive features. So, by identifying all high-scoring patches across all possible scales, the positive object can be detected by overlapping them to form a bounding box. The essential idea is that the object is detected by detecting its features on multiple scales, ranging from specific sub-features to abstract features composed of these sub-features. Training DisCNN requires only object-centered image data with positive and negative class labels. The detection process for multiple positive classes can be conducted in parallel to significantly accelerate it, and also faster for single-object detection because of its lightweight model architecture.

Related papers

• Distributed Convolutional Neural Networks for Object Recognition [5.912314032588749]
  This paper proposes a novel loss function for training a distributed convolutional neural network (DisCNN) to recognize only a specific positive class.<n>By mapping positive samples to a compact set in high-dimensional space and negative samples to Origin, the DisCNN extracts only the features of the positive class.
  arXiv  Detail & Related papers  (2026-03-10T05:40:45Z)
• Source-Free Object Detection with Detection Transformer [59.33653163035064]
  Source-Free Object Detection (SFOD) enables knowledge transfer from a source domain to an unsupervised target domain for object detection without access to source data.<n>Most existing SFOD approaches are either confined to conventional object detection (OD) models like Faster R-CNN or designed as general solutions without tailored adaptations for novel OD architectures, especially Detection Transformer (DETR)<n>In this paper, we introduce Feature Reweighting ANd Contrastive Learning NetworK (FRANCK), a novel SFOD framework specifically designed to perform query-centric feature enhancement for DETRs.
  arXiv  Detail & Related papers  (2025-10-13T07:35:04Z)
• 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)
• LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images [11.41884406231953]
  In this paper, we propose an effective lightweight oriented object detector (LO-Det) Specifically, a channel separation-aggregation (CSA) structure is designed to simplify the complexity of stacked separable convolutions. The proposed LO-Det can run very fast even on embedded devices with the competitive accuracy of detecting oriented objects.
  arXiv  Detail & Related papers  (2022-09-16T04:28:01Z)
• Multi-scale Interactive Network for Salient Object Detection [91.43066633305662]
  We propose the aggregate interaction modules to integrate the features from adjacent levels. To obtain more efficient multi-scale features, the self-interaction modules are embedded in each decoder unit. Experimental results on five benchmark datasets demonstrate that the proposed method without any post-processing performs favorably against 23 state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-07-17T15:41:37Z)
• Dynamic Refinement Network for Oriented and Densely Packed Object Detection [75.29088991850958]
  We present a dynamic refinement network that consists of two novel components, i.e., a feature selection module (FSM) and a dynamic refinement head (DRH) Our FSM enables neurons to adjust receptive fields in accordance with the shapes and orientations of target objects, whereas the DRH empowers our model to refine the prediction dynamically in an object-aware manner. We perform quantitative evaluations on several publicly available benchmarks including DOTA, HRSC2016, SKU110K, and our own SKU110K-R dataset.
  arXiv  Detail & Related papers  (2020-05-20T11:35:50Z)
• OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features [14.115782214599015]
  One-shot object detection consists in detecting objects defined by a single demonstration. We build the one-stage system that performs localization and recognition jointly. Experimental evaluation on several challenging domains shows that our method can detect unseen classes.
  arXiv  Detail & Related papers  (2020-03-15T11:39:47Z)
• BiDet: An Efficient Binarized Object Detector [96.19708396510894]
  We propose a binarized neural network learning method called BiDet for efficient object detection. Our BiDet fully utilizes the representational capacity of the binary neural networks for object detection by redundancy removal. Our method outperforms the state-of-the-art binary neural networks by a sizable margin.
  arXiv  Detail & Related papers  (2020-03-09T08:16:16Z)
• Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread [136.2224792151324]
  We propose a new deep learning algorithm for fast salient object detection. The proposed algorithm achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread.
  arXiv  Detail & Related papers  (2020-01-22T15:23:48Z)

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.