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There are tasks in computer vision where recurrent neural networks (RNNs) can be useful because there's some sequential sub-task in the main task. For instance, in the paper Long-Term Recurrent Convolutional Networks for Visual Recognition and Description, the authors investigate the use of a neural network that is both recurrent and convolutional to solve ...


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Yes, it is not specified because the region proposal algorithm did not change from R-CNN (the previous version from Fast R-CNN, however, in the next verion, Faster R-CNN, this algorithm is replaced by a CNN). The region proposal algorithm you are looking for is called selective search. You can find in the R-CNN paper that the algorithm is described in "...


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Is it possible to use SSL to pre-train e.g. a faster R-CNN on a pretext task (for example, rotation), then use this pre-trained model for instance segmentation with the aim to get better accuracy? Yes, it's possible and this has already been done. I don't know the details (because I have not yet read those papers), but I will provide you with some links to ...


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In my opinion, the second option will be more general. You can refer to some famous datasets for object detection task such as COCO or Pascal VOC, they usually accept the intersect annotations. As the image below, image from this link where they process the annotation of COCO dataset. I think the reason is that the model will be easier to separate the ...


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The ROIs in the input space are mapped to the feature map space, by dividing it by the net stride at that layer. Say, in a network, after a sequence of four 2x2 pooling layers, your image is reduced to 1/16 of the original size. (A 32*32 image is reduced to 2x2) So, the bounding boxes in the original space are mapped to the feature space by dividing by the ...


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