Tensor-to-Image: Image-to-Image Translation with Vision Transformers
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Abstract (arXiv)
Transformers gain huge attention since they are first introduced and have a wide range of applications. Transformers start to take over all areas of deep learning and the Vision transformers paper also proved that they can be used for computer vision tasks. In this paper, we utilized a vision transformer-based custom-designed model, tensor-to-image, for the image to image translation. With the help of self-attention, our model was able to generalize and apply to different problems without a single modification.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 6 Oct 2021.
Builds on
14 of 25 references.
References · 14
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
- 2016Improved Techniques for Training GANscited by 2,970
- 2016Context Encoders: Feature Learning by Inpaintingcited by 1,428
- 2019VisualBERT: A Simple and Performant Baseline for Vision and Languagecited by 1,296
- 2020Object-Centric Learning with Slot Attentioncited by 876
- 2019VideoBERT: A Joint Model for Video and Language Representation Learningcited by 703
- 2015Adversarial Autoencoderscited by 364
- 2017Relation Networks for Object Detectioncited by 305
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Citing papers per year: 2021: 1.
1 paper.