Consistent Multimodal Generation via A Unified GAN Framework

• URL: http://arxiv.org/abs/2307.01425v1
• Date: Tue, 4 Jul 2023 01:33:20 GMT
• Title: Consistent Multimodal Generation via A Unified GAN Framework
• Authors: Zhen Zhu, Yijun Li, Weijie Lyu, Krishna Kumar Singh, Zhixin Shu, Soeren Pirk, Derek Hoiem
• Abstract summary: We investigate how to generate multimodal image outputs, such as RGB, depth, and surface normals, with a single generative model. Our solution builds on the StyleGAN3 architecture, with a shared backbone and modality-specific branches in the last layers of the synthesis network. In experiments on the Stanford2D3D dataset, we demonstrate realistic and consistent generation of RGB, depth, and normal images.
• Score: 36.08519541540843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate how to generate multimodal image outputs, such as RGB, depth, and surface normals, with a single generative model. The challenge is to produce outputs that are realistic, and also consistent with each other. Our solution builds on the StyleGAN3 architecture, with a shared backbone and modality-specific branches in the last layers of the synthesis network, and we propose per-modality fidelity discriminators and a cross-modality consistency discriminator. In experiments on the Stanford2D3D dataset, we demonstrate realistic and consistent generation of RGB, depth, and normal images. We also show a training recipe to easily extend our pretrained model on a new domain, even with a few pairwise data. We further evaluate the use of synthetically generated RGB and depth pairs for training or fine-tuning depth estimators. Code will be available at https://github.com/jessemelpolio/MultimodalGAN.

Related papers

• Towards a Generalist and Blind RGB-X Tracker [91.36268768952755]
  We develop a single model tracker that can remain blind to any modality X during inference time. Our training process is extremely simple, integrating multi-label classification loss with a routing function. Our generalist and blind tracker can achieve competitive performance compared to well-established modal-specific models.
  arXiv  Detail & Related papers  (2024-05-28T03:00:58Z)
• General-Purpose Multimodal Transformer meets Remote Sensing Semantic Segmentation [35.100738362291416]
  Multimodal AI seeks to exploit complementary data sources, particularly for complex tasks like semantic segmentation. Recent trends in general-purpose multimodal networks have shown great potential to achieve state-of-the-art performance. We propose a UNet-inspired module that employs 3D convolution to encode vital local information and learn cross-modal features simultaneously.
  arXiv  Detail & Related papers  (2023-07-07T04:58:34Z)
• CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D Datasets [50.6643933702394]
  We present a single-model self-supervised hybrid pre-training framework for RGB and depth modalities, termed as CoMAE. Our CoMAE presents a curriculum learning strategy to unify the two popular self-supervised representation learning algorithms: contrastive learning and masked image modeling.
  arXiv  Detail & Related papers  (2023-02-13T07:09:45Z)
• HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness [2.341385717236931]
  We propose a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection. Our motivation comes from the observation that the multi-granularity properties of geometric priors correlate well with the neural network hierarchies. Our HiDAnet performs favorably over the state-of-the-art methods by large margins.
  arXiv  Detail & Related papers  (2023-01-18T10:00:59Z)
• Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection [67.33924278729903]
  In this work, we propose Dual Swin-Transformer based Mutual Interactive Network. We adopt Swin-Transformer as the feature extractor for both RGB and depth modality to model the long-range dependencies in visual inputs. Comprehensive experiments on five standard RGB-D SOD benchmark datasets demonstrate the superiority of the proposed DTMINet method.
  arXiv  Detail & Related papers  (2022-06-07T08:35:41Z)
• RGB-D Saliency Detection via Cascaded Mutual Information Minimization [122.8879596830581]
  Existing RGB-D saliency detection models do not explicitly encourage RGB and depth to achieve effective multi-modal learning. We introduce a novel multi-stage cascaded learning framework via mutual information minimization to "explicitly" model the multi-modal information between RGB image and depth data.
  arXiv  Detail & Related papers  (2021-09-15T12:31:27Z)
• Unpaired Single-Image Depth Synthesis with cycle-consistent Wasserstein GANs [1.0499611180329802]
  Real-time estimation of actual environment depth is an essential module for various autonomous system tasks. In this study, latest advancements in the field of generative neural networks are leveraged to fully unsupervised single-image depth synthesis.
  arXiv  Detail & Related papers  (2021-03-31T09:43:38Z)
• Self-Supervised Representation Learning for RGB-D Salient Object Detection [93.17479956795862]
  We use Self-Supervised Representation Learning to design two pretext tasks: the cross-modal auto-encoder and the depth-contour estimation. Our pretext tasks require only a few and un RGB-D datasets to perform pre-training, which make the network capture rich semantic contexts. For the inherent problem of cross-modal fusion in RGB-D SOD, we propose a multi-path fusion module.
  arXiv  Detail & Related papers  (2021-01-29T09:16:06Z)
• Adaptive Context-Aware Multi-Modal Network for Depth Completion [107.15344488719322]
  We propose to adopt the graph propagation to capture the observed spatial contexts. We then apply the attention mechanism on the propagation, which encourages the network to model the contextual information adaptively. Finally, we introduce the symmetric gated fusion strategy to exploit the extracted multi-modal features effectively. Our model, named Adaptive Context-Aware Multi-Modal Network (ACMNet), achieves the state-of-the-art performance on two benchmarks.
  arXiv  Detail & Related papers  (2020-08-25T06:00:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.