Saliency Detection is a preprocessing step in computer vision which aims at finding salient objects in an image.
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As an essential component of visual simultaneous localization and mapping (SLAM), place recognition is crucial for robot navigation and autonomous driving.
Many works have been done on salient object detection using supervised or unsupervised approaches on colour images.
To address the second challenge, we propose an Attention-based Multi-level Integrator Module to give the model the ability to assign different weights to multi-level feature maps.
The CCA can act as an efficient pixel-wise aggregation algorithm that can integrate state-of-the-art methods, resulting in even better results.
Existing state-of-the-art RGB-D salient object detection methods explore RGB-D data relying on a two-stream architecture, in which an independent subnetwork is required to process depth data.
Ranked #10 on RGB-D Salient Object Detection on NJU2K (Average MAE metric, using extra training data)
To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs).
We design a real-time portrait matting pipeline for everyday use, particularly for "virtual backgrounds" in video conferences.
Furthermore, the proposed DSCLSTM model can significantly boost the saliency detection performance by incorporating both global spatial interconnections and scene context modulation, which may uncover novel inspirations for studies on them in computational saliency models.