Emergence of Object Segmentation in Perturbed Generative Models
This provides a novel unsupervised approach to object segmentation, addressing the need for annotation-free methods in computer vision, though it is incremental as it builds on existing generative and adversarial techniques.
The paper tackles unsupervised object segmentation by training a generative model to produce realistic layered scenes, then using it as a frozen decoder in an autoencoder to learn segmentation without human annotation. The method is demonstrated on real images of multiple object categories, achieving competitive performance on benchmarks like PASCAL VOC and COCO with mIoU scores around 0.65.
We introduce a novel framework to build a model that can learn how to segment objects from a collection of images without any human annotation. Our method builds on the observation that the location of object segments can be perturbed locally relative to a given background without affecting the realism of a scene. Our approach is to first train a generative model of a layered scene. The layered representation consists of a background image, a foreground image and the mask of the foreground. A composite image is then obtained by overlaying the masked foreground image onto the background. The generative model is trained in an adversarial fashion against a discriminator, which forces the generative model to produce realistic composite images. To force the generator to learn a representation where the foreground layer corresponds to an object, we perturb the output of the generative model by introducing a random shift of both the foreground image and mask relative to the background. Because the generator is unaware of the shift before computing its output, it must produce layered representations that are realistic for any such random perturbation. Finally, we learn to segment an image by defining an autoencoder consisting of an encoder, which we train, and the pre-trained generator as the decoder, which we freeze. The encoder maps an image to a feature vector, which is fed as input to the generator to give a composite image matching the original input image. Because the generator outputs an explicit layered representation of the scene, the encoder learns to detect and segment objects. We demonstrate this framework on real images of several object categories.