skip to main content

Explore Research Products in the NSF-PAR

Title: Example-Guided Image Synthesis across Arbitrary Scenes using Masked Spatial-Channel Attention and Self-Supervision
Example-guided image synthesis has been recently attempted to synthesize an image from a semantic label map and an exemplary image. In the task, the additional exemplary image serves to provide style guidance that controls the appearance of the synthesized output. Despite the controllability advantage, the previous models are designed on datasets with specific and roughly aligned objects. In this paper, we tackle a more challenging and general task, where the exemplar is an arbitrary scene image that is semantically unaligned to the given label map. To this end, we first propose a new Masked Spatial-Channel Attention (MSCA) module which models the correspondence between two unstructured scenes via cross-attention. Next, we propose an end-to-end network for joint global and local feature alignment and synthesis. In addition, we propose a novel patch-based self-supervision scheme to enable training. Experiments on the large-scale CCOO-stuff dataset show significant improvements over existing methods. Moreover, our approach provides interpretability and can be readily extended to other tasks including style and spatial interpolation or extrapolation, as well as other content manipulation.  more » « less
Award ID(s):
1704337
NSF-PAR ID:
10168535
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
European Conference on Computer Vision
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ( , British Machine Vision Conference 2019)
    null (Ed.)
    Convolutional Neural Network (CNN) based image segmentation has made great progress in recent years. However, video object segmentation remains a challenging task due to its high computational complexity. Most of the previous methods employ a two-stream CNN framework to handle spatial and motion features separately. In this paper, we propose an end-to-end encoder-decoder style 3D CNN to aggregate spatial and temporal information simultaneously for video object segmentation. To efficiently process video, we propose 3D separable convolution for the pyramid pooling module and decoder, which dramatically reduces the number of operations while maintaining the performance. Moreover, we also extend our framework to video action segmentation by adding an extra classifier to predict the action label for actors in videos. Extensive experiments on several video datasets demonstrate the superior performance of the proposed approach for action and object segmentation compared to the state-of-the-art. 
    more » « less
  2. ; ( , IEEE MultiMedia)
    Sketch-to-image synthesis method transforms a simple abstract black-and-white sketch into an image. Most sketch-to-image synthesis methods generate an image in an end-to-end manner, leading to generate a non-satisfactory result. The reason is that, in end-to-end models, the models generate images directly from the input sketches. Thus, with very abstract and complicated sketches, the models might struggle in generating naturalistic images due to the simultaneous focus on both factors: overall shape and fine-grained details. In this paper, we propose to divide the problem into subproblems. To this end, an intermediate output, which is a semantic mask map, is first generated from the input sketch via an instance and semantic segmentation. In the instance segmentation stage, the objects' sizes might be modified depending on the surrounding environment and their respective size prior to reflect reality and produce more realistic images. In the semantic seg-mentation stage, a background segmentation is first constructed based on the context of the detected objects. Various natural scenes are implemented for both indoor and outdoor scenes. Following this, a foreground segmentation process is commenced, where each detected object is semantically added into the constructed segmented background. Then, in the next stage, an image-to-image translation model is leveraged to convert the semantic mask map into a colored image. Finally, a post-processing stage is incorporated to further enhance the image result. Extensive experiments demonstrate the superiority of our proposed method over state-of-the-art methods. 
    more » « less
  3. ; ; ; ; ; ( , ISBI)
    Multimodal medical image synthesis is an important task. Previous efforts mainly focus on the task domain of medical image synthesis using the complete source data and have achieved great success. However, data collection with completeness in real life might be prohibitive due to high expenses or other difficulties, particularly in brain imaging studies. In this paper, we address the challenging and important problem of medical image synthesis from incomplete multimodal data sources. We propose to learn the modal-wise representations and synthesize the targets accordingly. Particularly, a surrogate sampler is derived to generate the target representations from incomplete observations, based on which an interpretable attention-redistribution network is designed. The experimental results synthesizing PET images from MRI images demonstrate that the proposed method can solve different missing data scenarios and outperforms related baselines consistently. 
    more » « less
  4. ; ( , Multimedia Tools and Applications)
    Sketch-to-image is an important task to reduce the burden of creating a color image from scratch. Unlike previous sketch-to-image models, where the image is synthesized in an end-to-end manner, leading to an unnaturalistic image, we propose a method by decomposing the problem into subproblems to generate a more naturalistic and reasonable image. It first generates an intermediate output which is a semantic mask map from the input sketch through instance and semantic segmentation in two levels, background segmentation and foreground segmentation. Background segmentation is formed based on the context of the foreground objects. Then, the foreground segmentations are sequentially added to the created background segmentation. Finally, the generated mask map is fed into an image-to-image translation model to generate an image. Our proposed method works with 92 distinct classes. Compared to state-of-the-art sketch-to-image models, our proposed method outperforms the previous methods and generates better images. 
    more » « less
  5. ; ; ; ; ; ; ( , IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))
    We consider the problem of Vision-and-Language Navigation (VLN). The majority of current methods for VLN are trained end-to-end using either unstructured memory such as LSTM, or using cross-modal attention over the egocentric observations of the agent. In contrast to other works, our key insight is that the association between language and vision is stronger when it occurs in explicit spatial representations. In this work, we propose a cross-modal map learning model for vision-and-language navigation that first learns to predict the top-down semantics on an egocentric map for both observed and unobserved regions, and then predicts a path towards the goal as a set of way-points. In both cases, the prediction is informed by the language through cross-modal attention mechanisms. We experimentally test the basic hypothesis that language-driven navigation can be solved given a map, and then show competitive results on the full VLN-CE benchmark. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email