2022-05-01 更新
Multi-Curve Translator for Real-Time High-Resolution Image-to-Image Translation
Authors:Yuda Song, Hui Qian, Xin Du
The dominant image-to-image translation methods are based on fully convolutional networks, which extract and translate an image’s features and then reconstruct the image. However, they have unacceptable computational costs when working with high-resolution images. To this end, we present the Multi-Curve Translator (MCT), which not only predicts the translated pixels for the corresponding input pixels but also for their neighboring pixels. And if a high-resolution image is downsampled to its low-resolution version, the lost pixels are the remaining pixels’ neighboring pixels. So MCT makes it possible to feed the network only the downsampled image to perform the mapping for the full-resolution image, which can dramatically lower the computational cost. Besides, MCT is a plug-in approach that utilizes existing base models and requires only replacing their output layers. Experiments demonstrate that the MCT variants can process 4K images in real-time and achieve comparable or even better performance than the base models on various image-to-image translation tasks.
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Dual Diffusion Implicit Bridges for Image-to-Image Translation
Authors:Xuan Su, Jiaming Song, Chenlin Meng, Stefano Ermon
Common image-to-image translation methods rely on joint training over data from both source and target domains. This excludes cases where domain data is private (e.g., in a federated setting), and often means that a new model has to be trained for a new pair of domains. We present Dual Diffusion Implicit Bridges (DDIBs), an image translation method based on diffusion models, that circumvents training on domain pairs. DDIBs allow translations between arbitrary pairs of source-target domains, given independently trained diffusion models on the respective domains. Image translation with DDIBs is a two-step process: DDIBs first obtain latent encodings for source images with the source diffusion model, and next decode such encodings using the target model to construct target images. Moreover, DDIBs enable cycle-consistency by default and is theoretically connected to optimal transport. Experimentally, we apply DDIBs on a variety of synthetic and high-resolution image datasets, demonstrating their utility in example-guided color transfer, image-to-image translation as well as their connections to optimal transport methods.
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Translation between Molecules and Natural Language
Authors:Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Heng Ji
Joint representations between images and text have been deeply investigated in the literature. In computer vision, the benefits of incorporating natural language have become clear for enabling semantic-level control of images. In this work, we present $\textbf{MolT5}-$a self-supervised learning framework for pretraining models on a vast amount of unlabeled natural language text and molecule strings. $\textbf{MolT5}$ allows for new, useful, and challenging analogs of traditional vision-language tasks, such as molecule captioning and text-based de novo molecule generation (altogether: translation between molecules and language), which we explore for the first time. Furthermore, since $\textbf{MolT5}$ pretrains models on single-modal data, it helps overcome the chemistry domain shortcoming of data scarcity. Additionally, we consider several metrics, including a new cross-modal embedding-based metric, to evaluate the tasks of molecule captioning and text-based molecule generation. By interfacing molecules with natural language, we enable a higher semantic level of control over molecule discovery and understanding—a critical task for scientific domains such as drug discovery and material design. Our results show that $\textbf{MolT5}$-based models are able to generate outputs, both molecule and text, which in many cases are high quality and match the input modality. On molecule generation, our best model achieves 30% exact matching test accuracy (i.e., it generates the correct structure for about one-third of the captions in our held-out test set).
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Restricted Black-box Adversarial Attack Against DeepFake Face Swapping
Authors:Junhao Dong, Yuan Wang, Jianhuang Lai, Xiaohua Xie
DeepFake face swapping presents a significant threat to online security and social media, which can replace the source face in an arbitrary photo/video with the target face of an entirely different person. In order to prevent this fraud, some researchers have begun to study the adversarial methods against DeepFake or face manipulation. However, existing works focus on the white-box setting or the black-box setting driven by abundant queries, which severely limits the practical application of these methods. To tackle this problem, we introduce a practical adversarial attack that does not require any queries to the facial image forgery model. Our method is built on a substitute model persuing for face reconstruction and then transfers adversarial examples from the substitute model directly to inaccessible black-box DeepFake models. Specially, we propose the Transferable Cycle Adversary Generative Adversarial Network (TCA-GAN) to construct the adversarial perturbation for disrupting unknown DeepFake systems. We also present a novel post-regularization module for enhancing the transferability of generated adversarial examples. To comprehensively measure the effectiveness of our approaches, we construct a challenging benchmark of DeepFake adversarial attacks for future development. Extensive experiments impressively show that the proposed adversarial attack method makes the visual quality of DeepFake face images plummet so that they are easier to be detected by humans and algorithms. Moreover, we demonstrate that the proposed algorithm can be generalized to offer face image protection against various face translation methods.
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The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models
Authors:Julia Wolleb, Robin Sandkühler, Florentin Bieder, Philippe C. Cattin
Recently, diffusion models were applied to a wide range of image analysis tasks. We build on a method for image-to-image translation using denoising diffusion implicit models and include a regression problem and a segmentation problem for guiding the image generation to the desired output. The main advantage of our approach is that the guidance during the denoising process is done by an external gradient. Consequently, the diffusion model does not need to be retrained for the different tasks on the same dataset. We apply our method to simulate the aging process on facial photos using a regression task, as well as on a brain magnetic resonance (MR) imaging dataset for the simulation of brain tumor growth. Furthermore, we use a segmentation model to inpaint tumors at the desired location in healthy slices of brain MR images. We achieve convincing results for all problems.
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InstaFormer: Instance-Aware Image-to-Image Translation with Transformer
Authors:Soohyun Kim, Jongbeom Baek, Jihye Park, Gyeongnyeon Kim, Seungryong Kim
We present a novel Transformer-based network architecture for instance-aware image-to-image translation, dubbed InstaFormer, to effectively integrate global- and instance-level information. By considering extracted content features from an image as tokens, our networks discover global consensus of content features by considering context information through a self-attention module in Transformers. By augmenting such tokens with an instance-level feature extracted from the content feature with respect to bounding box information, our framework is capable of learning an interaction between object instances and the global image, thus boosting the instance-awareness. We replace layer normalization (LayerNorm) in standard Transformers with adaptive instance normalization (AdaIN) to enable a multi-modal translation with style codes. In addition, to improve the instance-awareness and translation quality at object regions, we present an instance-level content contrastive loss defined between input and translated image. We conduct experiments to demonstrate the effectiveness of our InstaFormer over the latest methods and provide extensive ablation studies.
PDF Accepted to CVPR 2022
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