Coarse-to-fine salient object detection based on deep convolutional neural networks

Li, Ying; Cui, Fan; Xue, Xizhe; Chan, Jonathan Cheung-Wai

doi:10.1016/j.image.2018.01.012

Cited by 13 publications

(6 citation statements)

References 37 publications

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“…In terms of feature learning, image features [28,29], color and texture features [30], complementary and discriminative features [31], as well as motion energy and appearance features [32] are learned to improve the coarse results. In terms of network architecture, the local superpixel-based CNN [33], fully convolutional network augmented with segmentation hypotheses [34], and the residual refinement block functioning as an internal module within the network [35] all represent viable approaches. The process from coarse to fine depends on how the coarse results are produced.…”

Section: Saliency Refinementmentioning

confidence: 99%

Robust and Refined Salient Object Detection Based on Diffusion Model

Ye,

Zhang,

Zhao

2023

Electronics

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Salient object detection (SOD) networks are vulnerable to adversarial attacks. As adversarial training is computationally expensive for SOD, existing defense methods instead adopt a noise-against-noise strategy that disrupts adversarial perturbation and restores the image either in input or feature space. However, their limited learning capacity and the need for network modifications limit their applicability. In recent years, the popular diffusion model coincides with the existing defense idea and exhibits excellent purification performance, but there still remains an accuracy gap between the saliency results generated from the purified images and the benign images. In this paper, we propose a Robust and Refined (RoRe) SOD defense framework based on the diffusion model to simultaneously achieve adversarial robustness as well as improved accuracy for benign and purified images. Our proposed RoRe defense consists of three modules: purification, adversarial detection, and refinement. The purification module leverages the powerful generation capability of the diffusion model to purify perturbed input images to achieve robustness. The adversarial detection module utilizes the guidance classifier in the diffusion model for multi-step voting classification. By combining this classifier with a similarity condition, precise adversarial detection can be achieved, providing the possibility of regaining the original accuracy for benign images. The refinement module uses a simple and effective UNet to enhance the accuracy of purified images. The experiments demonstrate that RoRe achieves superior robustness over state-of-the-art methods while maintaining high accuracy for benign images. Moreover, RoRe shows good results against backward pass differentiable approximation (BPDA) attacks.

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Section: Saliency Refinementmentioning

confidence: 99%

Robust and Refined Salient Object Detection Based on Diffusion Model

Ye,

Zhang,

Zhao

2023

Electronics

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“…CNN is a DL architecture that has proven to have excellent results when applied to image classification, resulting in classification rates of up to 100% accuracy [135]. CNN's operation can be explained as follows.…”

Section: Object Detection and Classificationmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

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There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.

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“…In addition, the public and private datasets used in this field to advance their application are discussed. In Reference 80 an accurate to fine approach is proposed that combines a pixel FCN with a super‐pixel‐based CNN to detect prominent objects with precise boundaries. In the first step, FCN is used to predict the overall pixel by pixel.…”

Section: Hba Related Modelsmentioning

confidence: 99%

A systematic survey of data mining and big data in human behavior analysis: Current datasets and models

Ding

Gan

Bahrami

2022

Trans Emerging Tel Tech

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In recent years, understanding human behavior has become one of the most important topics in the field of computer vision research. The reason for this growing attention is the wide range of applications that can benefit from the results of this field of research. The human behavior analysis (HBA) includes a wide range of research areas from the detection of human motion and action.Datasets created through actions and detection of human activities make it possible to compare different detection methods with the same input data. Data mining and big data approaches are very popular for analyzing data related to human behavior and can be used to address the challenges of fast processing. This article provides a systematic survey of data mining and big data in the HBA. We focus on current datasets and models related to the detection of human behavior patterns in the literature. The purpose of this survey is to assist researchers in select appropriate datasets and models to evaluate algorithms, as well as to identify research gaps for future work. To achieve this goal, articles published between 2010 and 2021 have been reviewed. These articles fall into five general categories in terms of dataset focus: object detection, motion, action, activity, and behavior. This article provides a summary of data mining and big data models in the HBA, as well as related datasets based on these categories, to highlight promising research avenues for future work.

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Coarse-to-fine salient object detection based on deep convolutional neural networks

Cited by 13 publications

References 37 publications

Robust and Refined Salient Object Detection Based on Diffusion Model

Robust and Refined Salient Object Detection Based on Diffusion Model

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

A systematic survey of data mining and big data in human behavior analysis: Current datasets and models

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