Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread

• URL: http://arxiv.org/abs/2001.08057v1
• Date: Wed, 22 Jan 2020 15:23:48 GMT
• Title: Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread
• Authors: Haofeng Li, Guanbin Li, Binbin Yang, Guanqi Chen, Liang Lin, Yizhou Yu
• Abstract summary: 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.
• Score: 136.2224792151324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently deep convolutional neural networks have achieved significant success in salient object detection. However, existing state-of-the-art methods require high-end GPUs to achieve real-time performance, which makes them hard to adapt to low-cost or portable devices. Although generic network architectures have been proposed to speed up inference on mobile devices, they are tailored to the task of image classification or semantic segmentation, and struggle to capture intra-channel and inter-channel correlations that are essential for contrast modeling in salient object detection. Motivated by the above observations, we design a new deep learning algorithm for fast salient object detection. The proposed algorithm for the first time achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread. Specifically, we propose a novel depthwise non-local moudule (DNL), which implicitly models contrast via harvesting intra-channel and inter-channel correlations in a self-attention manner. In addition, we introduce a depthwise non-local network architecture that incorporates both depthwise non-local modules and inverted residual blocks. Experimental results show that our proposed network attains very competitive accuracy on a wide range of salient object detection datasets while achieving state-of-the-art efficiency among all existing deep learning based algorithms.

Related papers

• RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture [24.824596231020585]
  We propose a packet-level network intrusion detection solution that makes use of Recurrent Autoencoders to integrate an arbitrary-length sequence of packets into a more compact joint feature embedding. We show that our approach leads to an extremely efficient, real-time solution with high detection accuracy at the packet level.
  arXiv  Detail & Related papers  (2023-11-27T17:30:19Z)
• 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)
• A Multi-task Framework for Infrared Small Target Detection and Segmentation [9.033048310220346]
  We propose a novel end-to-end framework for infrared small target detection and segmentation. We use UNet as the backbone to maintain resolution and semantic information. We develop a multi-task framework for infrared small target detection and segmentation.
  arXiv  Detail & Related papers  (2022-06-14T15:43:34Z)
• a novel attention-based network for fast salient object detection [14.246237737452105]
  In the current salient object detection network, the most popular method is using U-shape structure. We propose a new deep convolution network architecture with three contributions. Results demonstrate that the proposed method can compress the model to 1/3 of the original size nearly without losing the accuracy.
  arXiv  Detail & Related papers  (2021-12-20T12:30:20Z)
• Video Salient Object Detection via Contrastive Features and Attention Modules [106.33219760012048]
  We propose a network with attention modules to learn contrastive features for video salient object detection. A co-attention formulation is utilized to combine the low-level and high-level features. We show that the proposed method requires less computation, and performs favorably against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-11-03T17:40:32Z)
• Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection [4.683939045230724]
  Current object detectors are difficult to strike a balance between high accuracy and fast inference speed. We present an efficient and robust fully convolutional neural network model to perform robotic grasping pose estimation. The network is an order of magnitude smaller than other excellent algorithms.
  arXiv  Detail & Related papers  (2021-01-25T16:36:53Z)
• Attentional Local Contrast Networks for Infrared Small Target Detection [15.882749652217653]
  We propose a novel model-driven deep network for infrared small target detection. We modularize a conventional local contrast measure method as a depth-wise parameterless nonlinear feature refinement layer in an end-to-end network. We conduct detailed ablation studies with varying network depths to empirically verify the effectiveness and efficiency of each component in our network architecture.
  arXiv  Detail & Related papers  (2020-12-15T19:33:09Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)
• Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes [98.65457534223539]
  We propose a real-time high-performance DCNN-based method for robust semantic segmentation of urban street scenes. The proposed method achieves the accuracy of 73.6% and 68.0% mean Intersection over Union (mIoU) with the inference speed of 51.0 fps and 39.3 fps.
  arXiv  Detail & Related papers  (2020-03-11T08:45:53Z)
• 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)

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.