A method of annotation densification for semantic segmentation, the method comprising: obtaining image information concerning one or more first images; obtaining annotation information; generating at least one trimap using the annotation information, wherein the trimap defines a respective opaque foreground, a respective transparent background and respective unknown opacity regions; performing an image matting operation using the image information and using the at least one trimap, by way of at least one processing device; generating alpha map information using first intermediate information resulting from the image matting operation; applying an already-trained Convolutional Neural Network, ConvNet, semantic segmentation model using the image information, by way of the at least one processing device; generating probability map information using second intermediate information resulting from the applying of the already-trained ConvNet semantic segmentation model; performing a merging operation with respect to both the alpha map information and the probability map information, by way of the at least one processing device, wherein the performing of the merging operation results in fine semantic segmentation annotation information including a plurality of semantic labels pertaining respectively to a plurality of pixels of the one or more first images; training an additional semantic segmentation model using the fine semantic segmentation annotation information and image information; using the trained additional semantic segmentation model for inference to generate semantic segmentation output information; and outputting semantic segmentation output information, or other information or signal that utilizes said semantic segmentation output information, for receipt by another device or system, wherein the merging operation is a heuristics based merging operation and includes: determining a plurality of pixel values P i to be included in the fine semantic segmentation annotation information using: P i = arg max j 1 − r ⋅ p ij + r ⋅ normalize m ij, wherein mij are alpha values that constitute the alpha map information, pij constitutes the probability predicted by the ConvNet of a pixel i belonging to a category j, and r is the weight blending the alpha map and the probability map and has a value on or between 0 and 1. obtaining image information concerning one or more first images; obtaining annotation information; generating at least one trimap using the annotation information, wherein the trimap defines a respective opaque foreground, a respective transparent background and respective unknown opacity regions; performing an image matting operation using the image information and using the at least one trimap, by way of at least one processing device; generating alpha map information using first intermediate information resulting from the image matting operation; applying an already-trained Convolutional Neural Network, ConvNet, semantic segmentation model using the image information, by way of the at least one processing device; generating probability map information using second intermediate information resulting from the applying of the already-trained ConvNet semantic segmentation model; performing a merging operation with respect to both the alpha map information and the probability map information, by way of the at least one processing device, wherein the performing of the merging operation results in fine semantic segmentation annotation information including a plurality of semantic labels pertaining respectively to a plurality of pixels of the one or more first images; training an additional semantic segmentation model using the fine semantic segmentation annotation information and image information; using the trained additional semantic segmentation model for inference to generate semantic segmentation output information; and outputting semantic segmentation output information, or other information or signal that utilizes said semantic segmentation output information, for receipt by another device or system, wherein the merging operation is a heuristics based merging operation and includes: determining a plurality of pixel values P i to be included in the fine semantic segmentation annotation information using: P i = arg max j 1 − r ⋅ p ij + r ⋅ normalize m ij, wherein mij are alpha values that constitute the alpha map information, pij constitutes the probability predicted by the ConvNet of a pixel i belonging to a category j, and r is the weight blending the alpha map and the probability map and has a value on or between 0 and 1.