ECFFNet: Effective and Consistent Feature Fusion Network for RGB-T Salient Object Detection

Zhou, Wujie; Guo, Qinling; Lei, Jingsheng; Yu, Lu; Hwang, Jenq‐Neng

doi:10.1109/tcsvt.2021.3077058

Cited by 143 publications

(29 citation statements)

References 64 publications

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“…However, we simply concatenate the multilevel feature maps in the RF module as a feature fusion procedure, where the relationship among these maps is seldom considered. As we know, various fusion approaches, aiming to flexibly exploit the feature information, have successfully been presented in different scenarios [46–48]. Moreover, an attention mechanism has also been proved as a useful fusion strategy in image denoising as mentioned before.…”

Section: Discussionmentioning

confidence: 99%

Multiscale residual fusion network for image denoising

Yao

Tang

Sun

et al. 2021

IET Image Processing

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Deep‐learning methods have been developed in recent years and have achieved dramatic improvements for image denoising. The existing deep‐learning methods can be conducted using two major models: Encoder–decoder and high‐resolution, where the high‐resolution model has superior resolution ability for detail description and restoration. In this study, a high‐resolution‐based network called multiscale residual fusion network (MRF‐Net) is proposed, which employed the spatial and contextual information of images. In detail, dilated convolution layers are used to enlarge the network's receptive field and learned sufficient features in a multiscale feature extracting module. The function of dilated convolution is reinterpreted here and it is viewed as a complex downsampling operation. Therefore, multiscale feature analysis could be performed in the proposed network by dilated convolution. Multilevel feature maps are sequentially obtained through a residual projection module, where considerable contextual and spatial information was collected from the multiscale features. In a residual fusion module, all maps were aggregated to generate a residual image effectively for noise removal. Experiments demonstrated that the MRF‐Net outperformed several state‐of‐the‐art model‐based and deep‐learning methods in both blind and non‐blind image denoising tests. Meanwhile, ablation studies were executed to verify the denoising performance of each module. Moreover, this method exhibited high computational efficiency, thus demonstrating its practicability.

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Section: Discussionmentioning

confidence: 99%

Multiscale residual fusion network for image denoising

Yao

Tang

Sun

et al. 2021

IET Image Processing

View full text Add to dashboard Cite

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“…Most SOD methods [37,9,38,39,11,12] mainly focus on solving the integration of multi-level features to generate a salient map with accurate location and internal consistency. Other works are devoted to studying how to use multi-modal information, such as depth cues [40,41,42] or thermal infrared cues [43,44], as auxiliary inputs of the models to alleviate the defects of individual RGB sources, such as low-light environments and similar texture scenes. It is noted that, some methods [13,17,10,33] also introduce the edge detection as a joint or auxiliary task for SOD, which make the models pay more attention to the object structure and thus improve the localization of salient objects.…”

Section: Fully Supervised Salient Object Detectionmentioning

confidence: 99%

Scribble-based Boundary-aware Network for Weakly Supervised Salient Object Detection in Remote Sensing Images

Zhou¹,

Xiang²,

Chen³

et al. 2022

Preprint

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Existing CNNs-based salient object detection (SOD) heavily depends on the large-scale pixel-level annotations, which is labor-intensive, time-consuming, and expensive. By contrast, the sparse annotations (e.g., image-level or scribble) become appealing to the salient object detection community. However, few efforts are devoted to learning salient object detection from sparse annotations, especially in the remote sensing field. In addition, the sparse annotation usually contains scanty information, which makes it challenging to train a well-performing model, resulting in its performance largely lagging behind the fully-supervised models. Although some SOD methods adopt some prior cues (e.g., edges) to improve the detection performance, they usually lack targeted discrimination of object boundaries and thus provide saliency maps with poor boundary localization. To this end, in this paper, we propose a novel weaklysupervised salient object detection framework to predict the saliency of remote sensing images from sparse scribble annotations. To implement it, we first construct the scribble-based remote sensing saliency dataset by relabelling an existing large-scale SOD dataset with scribbles, namely S-EOR dataset. After that,

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“…Whereas, methods such as MFNet [62], RTFNet [63], PST900 [64], FuseSeg [65] combine the potential of RGB images along with thermal images using CNN architectures for semantic segmentation of outdoor scenes, providing accurate segmentation results even in presence of degraded lighting conditions. Authors of ECFFNet [66], perform the fusion of RGB and thermal images at feature level, which provides a complementary information effectively improving the object detection in different lighting conditions. Authors in [67] and [68] perform a fusion of RGB, depth and thermal camera computing descriptors in all the three image spaces and fusing them in a weighted average manner for efficient human detection.…”

Section: A Scene Understandingmentioning

confidence: 99%

From SLAM to Situational Awareness: Challenges and Survey

Bavle¹,

Sánchez-López²,

Cimarelli³

et al. 2021

Preprint

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The knowledge that an intelligent and autonomous mobile robot has and is able to acquire of itself and the environment, namely the situation, limits its reasoning, decision-making, and execution skills to efficiently and safely perform complex missions. Situational awareness is a basic capability of humans that has been deeply studied in fields like Psychology, Military, Aerospace, Education, etc., but it has barely been considered in robotics, which has focused on ideas such as sensing, perception, sensor fusion, state estimation, localization and mapping, spatial AI, etc. In our research, we connected the broad multidisciplinary existing knowledge on situational awareness with its counterpart in mobile robotics. In this paper, we survey the state-of-the-art robotics algorithms, we analyze the situational awareness aspects that have been covered by them, and we discuss their missing points. We found out that the existing robotics algorithms are still missing manifold important aspects of situational awareness. As a consequence, we conclude that these missing features are limiting the performance of robotic situational awareness, and further research is needed to overcome this challenge. We see this as an opportunity, and provide our vision for future research on robotic situational awareness.

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ECFFNet: Effective and Consistent Feature Fusion Network for RGB-T Salient Object Detection

Cited by 143 publications

References 64 publications

Multiscale residual fusion network for image denoising

Multiscale residual fusion network for image denoising

Scribble-based Boundary-aware Network for Weakly Supervised Salient Object Detection in Remote Sensing Images

From SLAM to Situational Awareness: Challenges and Survey

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