Computer Vision and Machine Learning

Rakshith Shetty (PhD Student)


Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection
F. Rezaeianaran, R. Shetty, R. Aljundi, D. O. Reino, S. Zhang and B. Schiele
ICCV 2021, IEEE/CVF International Conference on Computer Vision, 2021
Adversarial Content Manipulation for Analyzing and Improving Model Robustness
R. Shetty
PhD Thesis, Universität des Saarlandes, 2021
Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing
V. Agarwal, R. Shetty and M. Fritz
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020), 2020
Diverse and Relevant Visual Storytelling with Scene Graph Embeddings
X. Hong, R. Shetty, A. Sayeed, K. Mehra, V. Demberg and B. Schiele
Proceedings of the 24th Conference on Computational Natural Language Learning (CoNLL 2020), 2020
Towards Automated Testing and Robustification by Semantic Adversarial Data Generation
R. Shetty, M. Fritz and B. Schiele
Computer Vision -- ECCV 2020, 2020
Not Using the Car to See the Sidewalk: Quantifying and Controlling the Effects of Context in Classification and Segmentation
R. Shetty, B. Schiele and M. Fritz
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019), 2019
Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions
M. Wagner, H. Basevi, R. Shetty, W. Li, M. Malinowski, M. Fritz and A. Leonardis
Computer Vision - ECCV 2018 Workshops, 2018
Adversarial Scene Editing: Automatic Object Removal from Weak Supervision
R. Shetty, M. Fritz and B. Schiele
Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 2018
While great progress has been made recently in automatic image manipulation,<br>it has been limited to object centric images like faces or structured scene<br>datasets. In this work, we take a step towards general scene-level image<br>editing by developing an automatic interaction-free object removal model. Our<br>model learns to find and remove objects from general scene images using<br>image-level labels and unpaired data in a generative adversarial network (GAN)<br>framework. We achieve this with two key contributions: a two-stage editor<br>architecture consisting of a mask generator and image in-painter that<br>co-operate to remove objects, and a novel GAN based prior for the mask<br>generator that allows us to flexibly incorporate knowledge about object shapes.<br>We experimentally show on two datasets that our method effectively removes a<br>wide variety of objects using weak supervision only<br>
A4NT: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation
R. Shetty, B. Schiele and M. Fritz
Proceedings of the 27th USENIX Security Symposium, 2018
Image and Video Captioning with Augmented Neural Architectures
R. Shetty, H. R. Tavakoli and J. Laaksonen
IEEE MultiMedia, Volume 25, Number 2, 2018
Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training
R. Shetty, M. Rohrbach, L. A. Hendricks, M. Fritz and B. Schiele
IEEE International Conference on Computer Vision (ICCV 2017), 2017
Paying Attention to Descriptions Generated by Image Captioning Models
H. R. Tavakoli, R. Shetty, A. Borji and J. Laaksonen
IEEE International Conference on Computer Vision (ICCV 2017), 2017