Foreground is all we want!!! 🔥 🔥 Salient object detection(SOD) is a task-based on a visual attention mechanism, in which algorithms aim to explore objects or regions more attentive than the surrounding areas on the scene or images. 😃
Salient object detection(SOD) is a task-based on a visual attention mechanism, in which algorithms aim to explore objects or regions more attentive than the surrounding areas on the scene or images. 😃
SOD was not an interesting 😕 subject to DL researchers as compared to Image Recognition or Image Classification, therefore the Neural Networks developed were more focused on Image Recognition and Image Classification rather than SOD. The Neural Networks till now were developed by considering the problems of Image Classification or Image Recognition. Therefore on of the problem they faced researchers in the field of SOD faced because of using Image Classification or Image Recognition models like VGG, AlexNet, ResNet, etc were that these networks used to take additional features and were *deep *because of which they extracted a lot of fine details as compared to the contextual information.
Major Techniques Used:- 🔆
In the *Multi-level deep feature integration technique, *we take the patches of the images and try to extract the contextual features, another type of technique is *Multi-scale feature extraction *as shown in the picture below
It discards the approach of using the patches to extract contextual features, it uses a pyramid scheme i.e. 😻
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