Abstract: Compositing aware digital image search techniques and systems are described that leverage machine learning. In one example, a compositing aware image search system employs a two-stream convolutional neural network (CNN) to jointly learn feature embeddings from foreground digital images that capture a foreground object and background digital images that capture a background scene. In order to train models of the convolutional neural networks, triplets of training digital images are used. Each triplet may include a positive foreground digital image and a positive background digital image taken from the same digital image. The triplet also contains a negative foreground or background digital image that is dissimilar to the positive foreground or background digital image that is also included as part of the triplet.

Abstract: A method for scene parsing includes: performing a convolution operation on a to-be-parsed image by using a deep neural network to obtain a first feature map, the first feature map including features of at least one pixel in the image; performing a pooling operation on the first feature map to obtain at least one second feature map, a size of the second feature map being less than that of the first feature map; and performing scene parsing on the image according to the first feature map and the at least one second feature map to obtain a scene parsing result of the image, the scene parsing result including a category of the at least one pixel in the image. A system for scene parsing and a non-transitory computer-readable storage medium can facilitate realizing the method.

Abstract: Embodiments of the present application provide an image processing method and apparatus, an electronic device, a storage medium, and a program product. The method includes: generating a feature map of a to-be-processed image by performing feature extraction on the image; determining a feature weight corresponding to each of a plurality of feature points comprised in the feature map; and obtaining a feature-enhanced feature map by separately transmitting feature information of each feature point to associated other feature points comprised in the feature map based on the corresponding feature weight.

Abstract: Compositing aware digital image search techniques and systems are described that leverage machine learning. In one example, a compositing aware image search system employs a two-stream convolutional neural network (CNN) to jointly learn feature embeddings from foreground digital images that capture a foreground object and background digital images that capture a background scene. In order to train models of the convolutional neural networks, triplets of training digital images are used. Each triplet may include a positive foreground digital image and a positive background digital image taken from the same digital image. The triplet also contains a negative foreground or background digital image that is dissimilar to the positive foreground or background digital image that is also included as part of the triplet.

Abstract: Compositing aware digital image search techniques and systems are described that leverage machine learning. In one example, a compositing aware image search system employs a two-stream convolutional neural network (CNN) to jointly learn feature embeddings from foreground digital images that capture a foreground object and background digital images that capture a background scene. In order to train models of the convolutional neural networks, triplets of training digital images are used. Each triplet may include a positive foreground digital image and a positive background digital image taken from the same digital image. The triplet also contains a negative foreground or background digital image that is dissimilar to the positive foreground or background digital image that is also included as part of the triplet.

Abstract: Compositing aware digital image search techniques and systems are described that leverage machine learning. In one example, a compositing aware image search system employs a two-stream convolutional neural network (CNN) to jointly learn feature embeddings from foreground digital images that capture a foreground object and background digital images that capture a background scene. In order to train models of the convolutional neural networks, triplets of training digital images are used. Each triplet may include a positive foreground digital image and a positive background digital image taken from the same digital image. The triplet also contains a negative foreground or background digital image that is dissimilar to the positive foreground or background digital image that is also included as part of the triplet.

Abstract: A method for scene parsing includes: performing a convolution operation on a to-be-parsed image by using a deep neural network to obtain a first feature map, the first feature map including features of at least one pixel in the image; performing a pooling operation on the first feature map to obtain at least one second feature map, a size of the second feature map being less than that of the first feature map; and performing scene parsing on the image according to the first feature map and the at least one second feature map to obtain a scene parsing result of the image, the scene parsing result including a category of the at least one pixel in the image. A system for scene parsing and a non-transitory computer-readable storage medium can facilitate realizing the method.