In challenging low light and adverse weather conditions,thermal vision algorithms,especially object detection,have exhibited remarkable potential,contrasting with the frequent struggles encountered by visible vision algorithms. Nevertheless,the efficacy of thermal vision algorithms driven by deep learning models remains constrained by the paucity of available training data samples. To this end,this paper introduces a novel approach termed the edge guided conditional diffusion model. This framework aims to produce meticulously aligned pseudo thermal images at the pixel level,leveraging edge information extracted from visible images. By utilizing edges as contextual cues from the visible domain,the diffusion model achieves meticulous control over the delineation of objects within the generated images. To alleviate the impacts of those visible-specific edge information that should not appear in the thermal domain,a two-stage modality adversarial training strategy is proposed to filter them out from the generated images by differentiating the visible and thermal modality. Extensive experiments on LLVIP demonstrate ECDM s superiority over existing state-of-the-art approaches in terms of image generation quality.
MMoT: Mixture-of-Modality-Tokens Transformer for Composed Multimodal Conditional Image Synthesis
South China University of Technology（华南理工大学）；JD Explore Academy（京东探索研究院）；Nanyang Technological University（南洋理工大学）；Shanghai Jiao Tong University（上海交通大学）
Recovering badly damaged face images is a useful yet challenging task, especially in extreme cases where the masked or damaged region is very large. One of the major challenges is the ability of the system to generalize on faces outside the training dataset. We propose to tackle this extreme inpainting task with a conditional Generative Adversarial Network (GAN) that utilizes structural information, such as edges, as a prior condition. Edge information can be obtained from the partially masked image and a structurally similar image or a hand drawing. In our proposed conditional GAN, we pass th…
AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling
Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, China（中国科学院智能信息处理重点实验室（中国科学院计算技术研究所，中国））；University of Chinese Academy of Sciences, China（中国科学院大学）
University of Science and Technology of China（中国科学技术大学）；Microsoft Research（微软研究院）
Portrait editing is a popular subject in photo manipulation. The Generative Adversarial Network (GAN) advances the generating of realistic faces and allows more face editing. In this paper, we argue about three issues in existing techniques: diversity, quality, and controllability for portrait synthesis and editing. To address these issues, we propose a novel end-to-end learning framework that leverages conditional GANs guided by provided face masks for generating faces. The framework learns feature embeddings for every face component (e.g., mouth, hair, eye), separately, contributing to bette…
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Shandong University（山东大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）
Positron emission tomography (PET) imaging is an imaging modality for diagnosing a number of neurological diseases. In contrast to Magnetic Resonance Imaging (MRI), PET is costly and involves injecting a radioactive substance into the patient. Motivated by developments in modality transfer in vision, we study the generation of certain types of PET images from MRI data. We derive new flow-based generative models which we show perform well in this small sample size regime (much smaller than dataset sizes available in standard vision tasks). Our formulation, DUAL-GLOW, is based on two invertible …
Cocktail: Mixing Multi-Modality Controls for Text-Conditional Image Generation
Nanyang Technological University（南洋理工大学）；South China University of Technology（华南理工大学）；University of Oxford（牛津大学）；The University of Sydney（悉尼大学）；JD Explore Academy（京东探索研究院）
Centre Inria d’Université Côte d’Azur（蔚蓝海岸大学法国国家信息与自动化研究所中心）
Cross-modality data translation has attracted great interest in image computing. Deep generative models (\textit{e.g.}, GANs) show performance improvement in tackling those problems. Nevertheless, as a fundamental challenge in image translation, the problem of Zero-shot-Learning Cross-Modality Data Translation with fidelity remains unanswered. This paper proposes a new unsupervised zero-shot-learning method named Mutual Information guided Diffusion cross-modality data translation Model (MIDiffusion), which learns to translate the unseen source data to the target domain. The MIDiffusion leverag…
Plasma-CycleGAN: Plasma Biomarker-Guided MRI to PET Cross-modality Translation Using Conditional CycleGAN
School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；Banner Alzheimer’s Institute（班纳阿尔茨海默病研究所）；Basis Scottsdale（斯科茨代尔贝斯机构）；College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；Mayo Clinic Arizona（梅奥诊所亚利桑那州分院）
Peking University Shenzhen Graduate School（北京大学深圳研究生院）；KAUST（阿卜杜拉国王科技大学）
Recently, conditional diffusion models have gained popularity in numerous applications due to their exceptional generation ability. However, many existing methods are training-required. They need to train a time-dependent classifier or a condition-dependent score estimator, which increases the cost of constructing conditional diffusion models and is inconvenient to transfer across different conditions. Some current works aim to overcome this limitation by proposing training-free solutions, but most can only be applied to a specific category of tasks and not to more general conditions. In this …
Wayne State University（韦恩州立大学）；Henry Ford Health System（亨利·福特医疗系统）
Recently, interest in MR-only treatment planning using synthetic CTs (synCTs) has grown rapidly in radiation therapy. However, developing class solutions for medical images that contain atypical anatomy remains a major limitation. In this paper, we propose a novel spatial attention-guided generative adversarial network (attention-GAN) model to generate accurate synCTs using T1-weighted MRI images as the input to address atypical anatomy. Experimental results on fifteen brain cancer patients show that attention-GAN outperformed existing synCT models and achieved an average MAE of 85.22$\pm$12.0…
Human-Guided Shade Artifact Suppression in CBCT-to-MDCT Translation via Schrödinger Bridge with Conditional Diffusion
National Institute for Mathematical Sciences（国家信息科学研究院）；Department of Computer Engineering, Korea National University of Transportation（计算机工程系，韩国交通国立大学）
Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis
Emory University（埃默里大学）；Winship Cancer Institute（温希普癌症研究所）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
Transcranial MRI-guided focused ultrasound (TcMRgFUS) is a therapeutic ultrasound method that focuses sound through the skull to a small region noninvasively under MRI guidance. It is clinically approved to thermally ablate regions of the thalamus and is being explored for other therapies, such as blood brain barrier opening and neuromodulation. To accurately target ultrasound through the skull, the transmitted waves must constructively interfere at the target region. However, heterogeneity of the sound speed, density, and ultrasound attenuation in different individuals' skulls requires patien…
Duke University（杜克大学）；Duke University School of Medicine（杜克大学医学院）
Recent advancements in conditional Generative Adversarial Networks (cGANs) have shown promises in label guided image synthesis. Semantic masks, such as sketches and label maps, are another intuitive and effective form of guidance in image synthesis. Directly incorporating the semantic masks as constraints dramatically reduces the variability and quality of the synthesized results. We observe this is caused by the incompatibility of features from different inputs (such as mask image and latent vector) of the generator. To use semantic masks as guidance whilst providing realistic synthesized res…
In this work, we address the task of natural image generation guided by a conditioning input. We introduce a new architecture called conditional invertible neural network (cINN). The cINN combines the purely generative INN model with an unconstrained feed-forward network, which efficiently preprocesses the conditioning input into useful features. All parameters of the cINN are jointly optimized with a stable, maximum likelihood-based training procedure. By construction, the cINN does not experience mode collapse and generates diverse samples, in contrast to e.g. cGANs. At the same time our mod…
Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising
School of Computer and Big Data, Minjiang University（闽江学院计算机与大数据学院）；School of Information, Renmin University of China（中国人民大学信息学院）；RMIT University（皇家墨尔本理工大学）；Victoria University（维多利亚大学）
Universidad Católica San Pablo（圣巴勃罗天主教大学）；Université Paris-Est（巴黎东部大学）
To cope with the challenges that low light conditions produce in images, photographers tend to use the light provided by the camera flash to get better illumination. Nevertheless, harsh shadows and non-uniform illumination can arise from using a camera flash, especially in low light conditions. Previous studies have focused on normalizing the lighting on flash images; however, to the best of our knowledge, no prior studies have examined the sideways shadows removal, reconstruction of overexposed areas, and the generation of synthetic ambient shadows or natural tone of scene objects. To provide…
Shape-guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature
University of Leeds（利兹大学）；NIHR Leeds Biomedical Research Centre（NIHR利兹生物医学研究中心）；Alan Turing Institute（艾伦·图灵研究所）；KU Leuven（鲁汶大学）；University of Manchester（曼彻斯特大学）
针对大脑动脉环解剖变异数据稀缺问题，提出带形状与解剖引导的条件潜在扩散模型，用于生成逼真三维 CoW 分割，其血管连续性和视觉保真度优于 3D GAN/VAE，FID 较最佳 GAN 改善 53%。
University of Dundee（邓迪大学）；University of Cambridge（剑桥大学）；University of Bath（巴斯大学）；East China Normal University（华东师范大学）
MRI synthesis promises to mitigate the challenge of missing MRI modality in clinical practice. Diffusion model has emerged as an effective technique for image synthesis by modelling complex and variable data distributions. However, most diffusion-based MRI synthesis models are using a single modality. As they operate in the original image domain, they are memory-intensive and less feasible for multi-modal synthesis. Moreover, they often fail to preserve the anatomical structure in MRI. Further, balancing the multiple conditions from multi-modal MRI inputs is crucial for multi-modal synthesis. …
Sensetime Research（商汤科技研究院）；The Hong Kong Polytechnic University（香港理工大学）
Video highlight detection is a crucial yet challenging problem that aims to identify the interesting moments in untrimmed videos. The key to this task lies in effective video representations that jointly pursue two goals, \textit{i.e.}, cross-modal representation learning and fine-grained feature discrimination. In this paper, these two challenges are tackled by not only enriching intra-modality and cross-modality relations for representation modeling but also shaping the features in a discriminative manner. Our proposed method mainly leverages the intra-modality encoding and cross-modality co…
Fudan University（复旦大学）；University of Maryland（马里兰大学）
Videos are multimodal in nature. Conventional video recognition pipelines typically fuse multimodal features for improved performance. However, this is not only computationally expensive but also neglects the fact that different videos rely on different modalities for predictions. This paper introduces Hierarchical and Conditional Modality Selection (HCMS), a simple yet efficient multimodal learning framework for efficient video recognition. HCMS operates on a low-cost modality, i.e., audio clues, by default, and dynamically decides on-the-fly whether to use computationally-expensive modalitie…
Combining the respective advantages of cross-modality images can compensate for the lack of information in the single modality, which has attracted increasing attention of researchers into multi-modal image matching tasks. Meanwhile, due to the great appearance differences between cross-modality image pairs, it often fails to make the feature representations of correspondences as close as possible. In this letter, we design a cross-modality feature representation learning network, S2-Net, which is based on the recently successful detect-and-describe pipeline, originally proposed for visible im…
Recent RGBD-based models for saliency detection have attracted research attention. The depth clues such as boundary clues, surface normal, shape attribute, etc., contribute to the identification of salient objects with complicated scenarios. However, most RGBD networks require multi-modalities from the input side and feed them separately through a two-stream design, which inevitably results in extra costs on depth sensors and computation. To tackle these inconveniences, we present in this paper a novel fusion design named modality-guided subnetwork (MGSnet). It has the following superior desig…
University of Rochester（罗切斯特大学）；University of Chinese Academy of Sciences（中国科学院大学）；University of North Texas（北得克萨斯大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）
MAGE: View-guided Point Cloud Completion with Efficient Modality Alignment and Adaptive Geometry Enhancement
MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所MAIS研究室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；GIPSA-lab, Univ. Grenoble Alpes, CNRS, Grenoble INP（格勒诺布尔大学GIPSA实验室，法国国家科学研究中心，格勒诺布尔国立综合理工学院）
SGMA: Semantic-Guided Modality-Aware Segmentation for Remote Sensing with Incomplete Multimodal Data
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；Hangzhou Institute of Technology, Xidian University（西安电子科技大学杭州研究院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）
Mutual Information Guided Optimal Transport for Unsupervised Visible-Infrared Person Re-identification
School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；School of Informatics, Xiamen University（厦门大学信息学院）；Institute of Automation Chinese Academy of Sciences（中国科学院自动化研究所）；Nanjing University of Posts and Telecommunications（南京邮电大学）
RGBX-R1: Visual Modality Chain-of-Thought Guided Reinforcement Learning for Multimodal Grounding
School of Artificial Intelligence, Tianjin University（天津大学人工智能学院）；Low-Altitude Intelligence Lab, Xiong’an National Innovation Center（雄安国家创新中心低空智能实验室）；Xiong’an Guochuang Lantian Technology Co., Ltd.（雄安国创莲田科技有限公司）；College of Electronic Science and Technology, National University of Defense Technology（国防科技大学电子科学学院）
Chongqing University（重庆大学）；University of Delaware（特拉华大学）
Data augmentation is a powerful technique for improving the performance of the few-shot classification task. It generates more samples as supplements, and then this task can be transformed into a common supervised learning issue for solution. However, most mainstream data augmentation based approaches only consider the single modality information, which leads to the low diversity and quality of generated features. In this paper, we present a novel multi-modal data augmentation approach named Dizygotic Conditional Variational AutoEncoder (DCVAE) for addressing the aforementioned issue. DCVAE co…
China University of Geoscience（中国地质大学）；University of Chinese Academy of Sciences（中国科学院大学）
RGB-Infrared (IR) person re-identification aims to retrieve person-of-interest from heterogeneous cameras, easily suffering from large image modality discrepancy caused by different sensing wavelength ranges. Existing work usually minimizes such discrepancy by aligning domain distribution of global features, while neglecting the intra-modality structural relations between semantic parts. This could result in the network overly focusing on local cues, without considering long-range body part dependencies, leading to meaningless region representations. In this paper, we propose a graph-enabled d…
Text-guided Feature Disentanglement for Cross-modal Gait Recognition
Fujian Key Laboratory of Urban Intelligent Sensing and Computing, Xiamen University（福建城市智能感知与计算重点实验室，厦门大学）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（多媒体可信感知与高效计算重点实验室，中华人民共和国教育部，厦门大学）
Disentangled representation is a powerful technique to tackle domain shift problem in medical image analysis in unsupervised domain adaptation setting.However, previous methods only focus on exacting domain-invariant feature and ignore whether exacted feature is meaningful for downstream tasks.We propose a novel framework, called semantic-guided disentangled representation (SGDR), an effective method to exact semantically meaningful feature for segmentation task to improve performance of cross modality medical image segmentation in unsupervised domain adaptation setting. To exact the meaningfu…
Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking
School of Computer Sciences and Technology, China University of Mining and Technology（中国矿业大学计算机科学与技术学院）；Mine Digitization Engineering Research Center of the Ministry of Education, China（教育部 mine 数字化工程研究中心）
Nanjing University of Science and Technology（南京理工大学）；University of Macau（澳门大学）
Cross-spectrum depth estimation aims to provide a depth map in all illumination conditions with a pair of dual-spectrum images. It is valuable for autonomous vehicle applications when the vehicle is equipped with two cameras of different modalities. However, images captured by different-modality cameras can be photometrically quite different. Therefore, cross-spectrum depth estimation is a very challenging problem. Moreover, the shortage of large-scale open-source datasets also retards further research in this field. In this paper, we propose an unsupervised visible-light image guided cross-sp…
Massachusetts General Hospital（马萨诸塞总医院）；Harvard Medical School（哈佛医学院）
Multimodal MRI provides complementary and clinically relevant information to probe tissue condition and to characterize various diseases. However, it is often difficult to acquire sufficiently many modalities from the same subject due to limitations in study plans, while quantitative analysis is still demanded. In this work, we propose a unified conditional disentanglement framework to synthesize any arbitrary modality from an input modality. Our framework hinges on a cycle-constrained conditional adversarial training approach, where it can extract a modality-invariant anatomical feature with …
ModalPrompt: Towards Efficient Multimodal Continual Instruction Tuning with Dual-Modality Guided Prompt
State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA（多模态人工智能系统国家重点实验室）；School of Artificial Intelligence, UCAS（人工智能学院）；Centre for Artificial Intelligence and Robotics, HKISI-CAS（人工智能与机器人中心）
