New interactive annotation approach
Claimed to outperform Polygon-RNN++ and being 10x faster.
ArXiV: https://arxiv.org/pdf/1903.06874.pdf
YouTube: https://www.youtube.com/watch?v=ycD2BtO-QzU
Code: https://github.com/fidler-lab/curve-gcn
#PyTorch #annotation #release
Claimed to outperform Polygon-RNN++ and being 10x faster.
ArXiV: https://arxiv.org/pdf/1903.06874.pdf
YouTube: https://www.youtube.com/watch?v=ycD2BtO-QzU
Code: https://github.com/fidler-lab/curve-gcn
#PyTorch #annotation #release
YouTube
Fast Interactive Object Annotation with Curve-GCN
Paper is accepted by Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Paper link: https://arxiv.org/abs/1903.06874
Code is available at: https://github.com/fidler-lab/curve-gcn
Paper link: https://arxiv.org/abs/1903.06874
Code is available at: https://github.com/fidler-lab/curve-gcn
VirTex: Learning Visual Representations from Textual Annotations
The authors offer an alternative approach to pre-training backbones for CV tasks – using semantically dense captions to learn visual representations.
Recent methods have explored unsupervised pretraining to scale to vast quantities of unlabeled images. In contrast, the authors aim to learn high-quality visual representations from fewer images. They revisit supervised pretraining and seek data-efficient alternatives to classification-based pretraining.
VirTex (CNN + Transformer) is pre-trained on COCO captions. On downstream tasks it can reach performance similar to pre-training on ImageNet, but with 10x less images!
Paper: https://arxiv.org/abs/2006.06666
Code: https://github.com/kdexd/virtex
Site: https://kdexd.github.io/virtex/
#imagecaptioning #cv #visual #annotation #transformer #pretraining #transferlearning #deeplearning #paper
The authors offer an alternative approach to pre-training backbones for CV tasks – using semantically dense captions to learn visual representations.
Recent methods have explored unsupervised pretraining to scale to vast quantities of unlabeled images. In contrast, the authors aim to learn high-quality visual representations from fewer images. They revisit supervised pretraining and seek data-efficient alternatives to classification-based pretraining.
VirTex (CNN + Transformer) is pre-trained on COCO captions. On downstream tasks it can reach performance similar to pre-training on ImageNet, but with 10x less images!
Paper: https://arxiv.org/abs/2006.06666
Code: https://github.com/kdexd/virtex
Site: https://kdexd.github.io/virtex/
#imagecaptioning #cv #visual #annotation #transformer #pretraining #transferlearning #deeplearning #paper
