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"Image To Image Translation": models, code, and papers

Multimodal Machine Translation with Reinforcement Learning

May 07, 2018
Xin Qian, Ziyi Zhong, Jieli Zhou

Multimodal machine translation is one of the applications that integrates computer vision and language processing. It is a unique task given that in the field of machine translation, many state-of-the-arts algorithms still only employ textual information. In this work, we explore the effectiveness of reinforcement learning in multimodal machine translation. We present a novel algorithm based on the Advantage Actor-Critic (A2C) algorithm that specifically cater to the multimodal machine translation task of the EMNLP 2018 Third Conference on Machine Translation (WMT18). We experiment our proposed algorithm on the Multi30K multilingual English-German image description dataset and the Flickr30K image entity dataset. Our model takes two channels of inputs, image and text, uses translation evaluation metrics as training rewards, and achieves better results than supervised learning MLE baseline models. Furthermore, we discuss the prospects and limitations of using reinforcement learning for machine translation. Our experiment results suggest a promising reinforcement learning solution to the general task of multimodal sequence to sequence learning.

  

Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation

Mar 29, 2022
Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong, Kayhan Batmanghelich

Unpaired image-to-image translation (I2I) is an ill-posed problem, as an infinite number of translation functions can map the source domain distribution to the target distribution. Therefore, much effort has been put into designing suitable constraints, e.g., cycle consistency (CycleGAN), geometry consistency (GCGAN), and contrastive learning-based constraints (CUTGAN), that help better pose the problem. However, these well-known constraints have limitations: (1) they are either too restrictive or too weak for specific I2I tasks; (2) these methods result in content distortion when there is a significant spatial variation between the source and target domains. This paper proposes a universal regularization technique called maximum spatial perturbation consistency (MSPC), which enforces a spatial perturbation function (T ) and the translation operator (G) to be commutative (i.e., TG = GT ). In addition, we introduce two adversarial training components for learning the spatial perturbation function. The first one lets T compete with G to achieve maximum perturbation. The second one lets G and T compete with discriminators to align the spatial variations caused by the change of object size, object distortion, background interruptions, etc. Our method outperforms the state-of-the-art methods on most I2I benchmarks. We also introduce a new benchmark, namely the front face to profile face dataset, to emphasize the underlying challenges of I2I for real-world applications. We finally perform ablation experiments to study the sensitivity of our method to the severity of spatial perturbation and its effectiveness for distribution alignment.

* CVPR 2022 accepted paper 
  

Learning Structure-Appearance Joint Embedding for Indoor Scene Image Synthesis

Dec 09, 2019
Yuan Xue, Zihan Zhou, Xiaolei Huang

Advanced image synthesis methods can generate photo-realistic images for faces, birds, bedrooms, and more. However, these methods do not explicitly model and preserve essential structural constraints such as junctions, parallel lines, and planar surfaces. In this paper, we study the problem of structured indoor image generation for design applications. We utilize a small-scale dataset that contains both images of various indoor scenes and their corresponding ground-truth wireframe annotations. While existing image synthesis models trained on the dataset are insufficient in preserving structural integrity, we propose a novel model based on a structure-appearance joint embedding learned from both images and wireframes. In our model, structural constraints are explicitly enforced by learning a joint embedding in a shared encoder network that must support the generation of both images and wireframes. We demonstrate the effectiveness of the joint embedding learning scheme on the indoor scene wireframe to image translation task. While wireframes as input contain less semantic information than inputs of other traditional image translation tasks, our model can generate high fidelity indoor scene renderings that match well with input wireframes. Experiments on a wireframe-scene dataset show that our proposed translation model significantly outperforms existing state-of-the-art methods in both visual quality and structural integrity of generated images.

  

Cross-Modality Sub-Image Retrieval using Contrastive Multimodal Image Representations

Jan 10, 2022
Eva Breznik, Elisabeth Wetzer, Joakim Lindblad, Nataša Sladoje

In tissue characterization and cancer diagnostics, multimodal imaging has emerged as a powerful technique. Thanks to computational advances, large datasets can be exploited to improve diagnosis and discover patterns in pathologies. However, this requires efficient and scalable image retrieval methods. Cross-modality image retrieval is particularly demanding, as images of the same content captured in different modalities may display little common information. We propose a content-based image retrieval system (CBIR) for reverse (sub-)image search to retrieve microscopy images in one modality given a corresponding image captured by a different modality, where images are not aligned and share only few structures. We propose to combine deep learning to generate representations which embed both modalities in a common space, with classic, fast, and robust feature extractors (SIFT, SURF) to create a bag-of-words model for efficient and reliable retrieval. Our application-independent approach shows promising results on a publicly available dataset of brightfield and second harmonic generation microscopy images. We obtain 75.4% and 83.6% top-10 retrieval success for retrieval in one or the other direction. Our proposed method significantly outperforms both direct retrieval of the original multimodal (sub-)images, as well as their corresponding generative adversarial network (GAN)-based image-to-image translations. We establish that the proposed method performs better in comparison with a recent sub-image retrieval toolkit, GAN-based image-to-image translations, and learnt feature extractors for the downstream task of cross-modal image retrieval. We highlight the shortcomings of the latter methods and observe the importance of equivariance and invariance properties of the learnt representations and feature extractors in the CBIR pipeline. Code will be available at github.com/MIDA-group.

  

Generative Adversarial Network Applications in Creating a Meta-Universe

Jan 23, 2022
Soheyla Amirian, Thiab R. Taha, Khaled Rasheed, Hamid R. Arabnia

Generative Adversarial Networks (GANs) are machine learning methods that are used in many important and novel applications. For example, in imaging science, GANs are effectively utilized in generating image datasets, photographs of human faces, image and video captioning, image-to-image translation, text-to-image translation, video prediction, and 3D object generation to name a few. In this paper, we discuss how GANs can be used to create an artificial world. More specifically, we discuss how GANs help to describe an image utilizing image/video captioning methods and how to translate the image to a new image using image-to-image translation frameworks in a theme we desire. We articulate how GANs impact creating a customized world.

* Computational Science and Computational Intelligence; 2021 International Conference on IEEE CPS (IEEE XPLORE, Scopus), IEEE, 2021 
  

Cali-Sketch: Stroke Calibration and Completion for High-Quality Face Image Generation from Poorly-Drawn Sketches

Nov 01, 2019
Weihao Xia, Yujiu Yang, Jing-Hao Xue

Image generation task has received increasing attention because of its wide application in security and entertainment. Sketch-based face generation brings more fun and better quality of image generation due to supervised interaction. However, When a sketch poorly aligned with the true face is given as input, existing supervised image-to-image translation methods often cannot generate acceptable photo-realistic face images. To address this problem, in this paper we propose Cali-Sketch, a poorly-drawn-sketch to photo-realistic-image generation method. Cali-Sketch explicitly models stroke calibration and image generation using two constituent networks: a Stroke Calibration Network (SCN), which calibrates strokes of facial features and enriches facial details while preserving the original intent features; and an Image Synthesis Network (ISN), which translates the calibrated and enriched sketches to photo-realistic face images. In this way, we manage to decouple a difficult cross-domain translation problem into two easier steps. Extensive experiments verify that the face photos generated by Cali-Sketch are both photo-realistic and faithful to the input sketches, compared with state-of-the-art methods

* 10 pages, 12 figures 
  

Semantics-aware Multi-modal Domain Translation:From LiDAR Point Clouds to Panoramic Color Images

Jun 26, 2021
Tiago Cortinhal, Fatih Kurnaz, Eren Aksoy

In this work, we present a simple yet effective framework to address the domain translation problem between different sensor modalities with unique data formats. By relying only on the semantics of the scene, our modular generative framework can, for the first time, synthesize a panoramic color image from a given full 3D LiDAR point cloud. The framework starts with semantic segmentation of the point cloud, which is initially projected onto a spherical surface. The same semantic segmentation is applied to the corresponding camera image. Next, our new conditional generative model adversarially learns to translate the predicted LiDAR segment maps to the camera image counterparts. Finally, generated image segments are processed to render the panoramic scene images. We provide a thorough quantitative evaluation on the SemanticKitti dataset and show that our proposed framework outperforms other strong baseline models. Our source code is available at https://github.com/halmstad-University/TITAN-NET

  

PoseGAN: A Pose-to-Image Translation Framework for Camera Localization

Jun 23, 2020
Kanglin Liu, Qing Li, Guoping Qiu

Camera localization is a fundamental requirement in robotics and computer vision. This paper introduces a pose-to-image translation framework to tackle the camera localization problem. We present PoseGANs, a conditional generative adversarial networks (cGANs) based framework for the implementation of pose-to-image translation. PoseGANs feature a number of innovations including a distance metric based conditional discriminator to conduct camera localization and a pose estimation technique for generated camera images as a stronger constraint to improve camera localization performance. Compared with learning-based regression methods such as PoseNet, PoseGANs can achieve better performance with model sizes that are 70% smaller. In addition, PoseGANs introduce the view synthesis technique to establish the correspondence between the 2D images and the scene, \textit{i.e.}, given a pose, PoseGANs are able to synthesize its corresponding camera images. Furthermore, we demonstrate that PoseGANs differ in principle from structure-based localization and learning-based regressions for camera localization, and show that PoseGANs exploit the geometric structures to accomplish the camera localization task, and is therefore more stable than and superior to learning-based regressions which rely on local texture features instead. In addition to camera localization and view synthesis, we also demonstrate that PoseGANs can be successfully used for other interesting applications such as moving object elimination and frame interpolation in video sequences.

  

From Design Draft to Real Attire: Unaligned Fashion Image Translation

Sep 16, 2020
Yu Han, Shuai Yang, Wenjing Wang, Jiaying Liu

Fashion manipulation has attracted growing interest due to its great application value, which inspires many researches towards fashion images. However, little attention has been paid to fashion design draft. In this paper, we study a new unaligned translation problem between design drafts and real fashion items, whose main challenge lies in the huge misalignment between the two modalities. We first collect paired design drafts and real fashion item images without pixel-wise alignment. To solve the misalignment problem, our main idea is to train a sampling network to adaptively adjust the input to an intermediate state with structure alignment to the output. Moreover, built upon the sampling network, we present design draft to real fashion item translation network (D2RNet), where two separate translation streams that focus on texture and shape, respectively, are combined tactfully to get both benefits. D2RNet is able to generate realistic garments with both texture and shape consistency to their design drafts. We show that this idea can be effectively applied to the reverse translation problem and present R2DNet accordingly. Extensive experiments on unaligned fashion design translation demonstrate the superiority of our method over state-of-the-art methods. Our project website is available at: https://victoriahy.github.io/MM2020/ .

* Accepted by ACMMM 2020. Our project website is available at: https://victoriahy.github.io/MM2020/ 
  
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