Anurag Das

I am currently, a PhD student at the Computer vision group at MPI Informatics in Saarbrucken, supervised by Prof. Bernt Schiele. Prior to that, I was a part of preparatory phase of Saarbrucken Graduate School of Computer Science. I did my bachelor's in Information Technology from IIIT Allahabad from 2013-17. My research focuses on Label Efficient Learning for vision tasks such as semantic segmentation, object detection etc.

Email / CV / Google Scholar / Twitter / Github

Publications

	MTA-CLIP: Language-Guided Semantic Segmentation with Mask-Text Alignment Anurag Das, Xinting Hu, Li Jiang, Bernt Schiele ECCV, 2024 We propose mask-text alignment based framework for improved semantic segmentation result. We show how mask-text alignment is better than pixel-text alignment for dense semantic segmentation task.
	Improving 2D Feature Representations by 3D-Aware Fine-Tuning Yuanwen Yue, Anurag Das, Francis Engelmann, Siyu Tang, Jan Eric Lenssen ECCV, 2024 We propose a 3D-aware finetuning of 2D foundational models, resulting in improved performance on downstream tasks such as semantic segmentation and depth estimation.
	Weakly-Supervised Domain Adaptive Semantic Segmentation with Prototypical Contrastive Learning Anurag Das, Yongqin Xian, Dengxin Dai, Bernt Schiele CVPR, 2023 We propose a common framework to use different weak labels, e.g., image, point and coarse labels from the target domain to reduce the performance gap between UDA and supervised learning.
	Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation Anurag Das, Yongqin Xian, Yang He, Zeynep Akata, Bernt Schiele WACV, 2023 We propose to utlize cheaper coarse annotations for urban scene semantic segmentation. Coarse annotation lacks fine Boundary details and are faster to annotate. Our proposed method obtains competitive performance with coarse annotation along with relatively free synthetic data compared with fine annotation at a fraction of the annotation budget.
	(SP)2Net for Generalized Zero-Label Semantic Segmentation Anurag Das, Yongqin Xian, Yang He, Zeynep Akata, Bernt Schiele GCPR, 2021 [ Best Master Thesis Award in Germany] Generalized Zero-shot Semantic Segmentation(GZSS) is a challenging problem as the prior works don't generalize well on unseen classes. we propose to leverage a class-agnostic segmentation prior provided by superpixels and introduce a superpixel pooling (SP-pooling) module that improves the performance on GZSS task.

Thesis

Master Thesis

Generalised Zero-shot semantic segmentation with superpixels
Saarland University, 2021

Academics

Reviewer: CVPR23, ECCV23, PAMI, IJCV, TMLR, IEEE-TCVST
2021: DAGM Best Master Thesis Award 2021 in Germany
2021: Saarland Stipendium funded by DAAD
2019: Saarbrucken Graduate School of Computer Science Scholarship
2017: Institute Merit Scholarship (IIIT Allahabad)
2011: Qualified for Indian National Mathematical Olympiad (INMO)

Stolen from Jon Barron. Big thanks!