Object segmentation is a crucial problem that is usually solved by using supervised learning approaches over very large datasets composed of both images and corresponding object masks. Since the masks have to be provided at pixel level, building such a dataset for any new domain can be very costly. We present ReDO, a new model able to extract objects from images without any annotation in an unsupervised way. It relies on the idea that it should be possible to change the textures or colors of the objects without changing the overall distribution of the dataset. Following this assumption, our approach is based on an adversarial architecture where the generator is guided by an input sample: given an image, it extracts the object mask, then redraws a new object at the same location. The generator is controlled by a discriminator that ensures that the distribution of generated images is aligned to the original one. We experiment with this method on different datasets and demonstrate the good quality of extracted masks.
@cite_8: The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence. Where those basic building blocks share meaningful properties, interactions and other regularities across scenes, such decompositions can simplify reasoning and facilitate imagination of novel scenarios. In particular, representing perceptual observations in terms of entities should improve data efficiency and transfer performance on a wide range of tasks. Thus we need models capable of discovering useful decompositions of scenes by identifying units with such regularities and representing them in a common format. To address this problem, we have developed the Multi-Object Network (MONet). In this model, a VAE is trained end-to-end together with a recurrent attention network -- in a purely unsupervised manner -- to provide attention masks around, and reconstructions of, regions of images. We show that this model is capable of learning to decompose and represent challenging 3D scenes into semantically meaningful components, such as objects and background elements.
@cite_32: This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distinguished from real data with the naked eye.
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@cite_45: We present LR-GAN: an adversarial image generation model which takes scene structure and context into account. Unlike previous generative adversarial networks (GANs), the proposed GAN learns to generate image background and foregrounds separately and recursively, and stitch the foregrounds on the background in a contextually relevant manner to produce a complete natural image. For each foreground, the model learns to generate its appearance, shape and pose. The whole model is unsupervised, and is trained in an end-to-end manner with conventional gradient descent methods. The experiments demonstrate that LR-GAN can generate more natural images with objects that are more human recognizable than baseline GANs.
@cite_15: Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities. Yet most work on representation learning focuses on feature learning without even considering multiple objects, or treats segmentation as an (often supervised) preprocessing step. Instead, we argue for the importance of learning to segment and represent objects jointly. We demonstrate that, starting from the simple assumption that a scene is composed of multiple entities, it is possible to learn to segment images into interpretable objects with disentangled representations. Our method learns -- without supervision -- to inpaint occluded parts, and extrapolates to scenes with more objects and to unseen objects with novel feature combinations. We also show that, due to the use of iterative variational inference, our system is able to learn multi-modal posteriors for ambiguous inputs and extends naturally to sequences.
@cite_13: Deep image generation is becoming a tool to enhance artists and designers creativity potential. In this paper, we aim at making the generation process easier to understand and interact with. Inspired by vector graphics systems, we propose a new deep generation paradigm where the images are composed of simple layers, defined by their color and a parametric transparency mask. This presents a number of advantages compared to the commonly used convolutional network architectures. In particular, our layered decomposition allows simple user interaction, for example to update a given mask, or change the color of a selected layer. From a compact code, our architecture also generates images with a virtually infinite resolution, the color at each point in an image being a parametric function of its coordinates. We validate the viability of our approach in the auto-encoding framework by comparing reconstructions with state-of-the-art baselines given similar memory resources on CIFAR10, CelebA and ImageNet datasets and demonstrate several applications. We also show Generative Adversarial Network (GAN) results qualitatively different from the ones obtained with common approaches.
Another related line of work relies on the idea of inferring scene decomposition directly from data. Stemming from DRAW @cite_32 , many of those approaches @cite_8 @cite_0 use an attention network to read a region of an image and a Variational Auto-encoder (VAE) to partially reconstruct the image in an iterative process in order to flesh out a meaningful decomposition. The very recent IODINE @cite_15 proposes a VAE adapted for multi-objects representation. LR-GAN @cite_45 is able to generate simple scenes recursively, building object after object, and 2018 @cite_13 decompose an image into single-colored strokes for vector graphics. While iterative processes have the advantage of being able to handle an arbitrary number of objects, they are also more unstable and difficult to train. Most of those can either only be used in generation @cite_45 , or only handle very simple objects @cite_8 @cite_0 @cite_27 . As a proof of concept, we decided to first ignore this additional difficulty by only handling a set number of objects but our model can naturally be extended with an iterative composition process.