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Found 12 papers, 4 papers with code
Unifying Generation and Compression: Ultra-low bitrate Image Coding Via Multi-stage Transformer
Recent progress in generative compression technology has significantly improved the perceptual quality of compressed data.
Scalable Face Image Coding via StyleGAN Prior: Towards Compression for Human-Machine Collaborative Vision
The accelerated proliferation of visual content and the rapid development of machine vision technologies bring significant challenges in delivering visual data on a gigantic scale, which shall be effectively represented to satisfy both human and machine requirements.
MAG-Edit: Localized Image Editing in Complex Scenarios via Mask-Based Attention-Adjusted Guidance
Recent diffusion-based image editing approaches have exhibited impressive editing capabilities in images with simple compositions.
Extreme Image Compression using Fine-tuned VQGANs
Remarkably, even with the loss of up to $20\%$ of indices, the images can be effectively restored with minimal perceptual loss.
Conceptual Compression via Deep Structure and Texture Synthesis
To this end, we propose a novel conceptual compression framework that encodes visual data into compact structure and texture representations, then decodes in a deep synthesis fashion, aiming to achieve better visual reconstruction quality, flexible content manipulation, and potential support for various vision tasks.
Continuous and Diverse Image-to-Image Translation via Signed Attribute Vectors
Generating a smooth sequence of intermediate results bridges the gap of two different domains, facilitating the morphing effect across domains.
DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
In this work, we present an approach based on disentangled representation for generating diverse outputs without paired training images.
Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
In this work, we propose a simple yet effective regularization term to address the mode collapse issue for cGANs.
Multimodal Unsupervised Image-To-Image Translation
Translation
A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation
As showcases, models that can learn a spanning tree or a weighted undirected $\ell_1$ graph are proposed, and a new learning algorithm is developed that learns a set of principal points and a graph structure from data, simultaneously.
A Feature Selection Method for Multivariate Performance Measures
The analyses by comparing with the state-of-the-art feature selection methods show that the proposed method is superior to others.
Efficient Multi-Template Learning for Structured Prediction
To alleviate this issue, in this paper, we propose a novel multiple template learning paradigm to learn structured prediction and the importance of each template simultaneously, so that hundreds of arbitrary templates could be added into the learning model without caution.
