Context Compression

Autoregressive Initial Bits (ArIB) a framework that combines subimage autoregression and latent variable models has shown its advantages in lossless image compression. However in current methods the image splitting makes the information of latent variables being uniformly distributed in each subimage and causes inadequate use of latent variables in addition to posterior collapse. To tackle these issues we introduce Bit Plane Slicing (BPS) splitting images in the bit plane dimension with the considerations on different importance for latent variables. Thus BPS provides a more effective representation by arranging subimages with decreasing importance for latent variables. To solve the problem of the increased number of dimensions caused by BPS we further propose a dimension-tailored autoregressive model that tailors autoregression methods for each dimension based on their characteristics efficiently capturing the dependencies in plane space and color dimensions. As shown in the extensive experimental results our method demonstrates the superior compression performance with comparable inference speed when compared to the state-of-the-art normalizing-flow-based methods. The code is at https://github.com/ZZ022/ArIB-BPS.

We introduce in-context matting a novel task setting of image matting. Given a reference image of a certain foreground and guided priors such as points scribbles and masks in-context matting enables automatic alpha estimation on a batch of target images of the same foreground category without additional auxiliary input. This setting marries good performance in auxiliary input-based matting and ease of use in automatic matting which finds a good trade-off between customization and automation. To overcome the key challenge of accurate foreground matching we introduce IconMatting an in-context matting model built upon a pre-trained text-to-image diffusion model. Conditioned on inter- and intra-similarity matching IconMatting can make full use of reference context to generate accurate target alpha mattes. To benchmark the task we also introduce a novel testing dataset ICM-57 covering 57 groups of real-world images. Quantitative and qualitative results on the ICM-57 testing set show that IconMatting rivals the accuracy of trimap-based matting while retaining the automation level akin to automatic matting. Code is available at https://github.com/tiny-smart/in-context-matting.

StyleGAN has shown remarkable performance in unconditional image generation. However its high computational cost poses a significant challenge for practical applications. Although recent efforts have been made to compress StyleGAN while preserving its performance existing compressed models still lag behind the original model particularly in terms of sample diversity. To overcome this we propose a novel channel pruning method that leverages varying sensitivities of channels to latent vectors which is a key factor in sample diversity. Specifically by assessing channel importance based on their sensitivities to latent vector perturbations our method enhances the diversity of samples in the compressed model. Since our method solely focuses on the channel pruning stage it has complementary benefits with prior training schemes without additional training cost. Extensive experiments demonstrate that our method significantly enhances sample diversity across various datasets. Moreover in terms of FID scores our method not only surpasses state-of-the-art by a large margin but also achieves comparable scores with only half training iterations.

This paper addresses the challenge of learning a local visual pattern of an object from one image and generating images depicting objects with that pattern. Learning a localized concept and placing it on an object in a target image is a nontrivial task as the objects may have different orientations and shapes. Our approach builds upon recent advancements in visual concept learning. It involves acquiring a visual concept (e.g. an ornament) from a source image and subsequently applying it to an object (e.g. a chair) in a target image. Our key idea is to perform in-context concept learning acquiring the local visual concept within the broader context of the objects they belong to. To localize the concept learning we employ soft masks that contain both the concept within the mask and the surrounding image area. We demonstrate our approach through object generation within an image showcasing plausible embedding of in-context learned concepts. We also introduce methods for directing acquired concepts to specific locations within target images employing cross-attention mechanisms and establishing correspondences between source and target objects. The effectiveness of our method is demonstrated through quantitative and qualitative experiments along with comparisons against baseline techniques.