no code implementations • ECCV 2020 • Rakshith Shetty, Mario Fritz, Bernt Schiele
Constrained adversarial optimization of object appearance through this synthesizer produces rare/difficult versions of an object which fool the target object detector.
1 code implementation • ICCV 2021 • Farzaneh Rezaeianaran, Rakshith Shetty, Rahaf Aljundi, Daniel Olmeda Reino, Shanshan Zhang, Bernt Schiele
In order to robustly deploy object detectors across a wide range of scenarios, they should be adaptable to shifts in the input distribution without the need to constantly annotate new data.
Multi-Source Unsupervised Domain Adaptation Object Detection +1
no code implementations • CONLL 2020 • Xudong Hong, Rakshith Shetty, Asad Sayeed, Khushboo Mehra, Vera Demberg, Bernt Schiele
A problem in automatically generated stories for image sequences is that they use overly generic vocabulary and phrase structure and fail to match the distributional characteristics of human-generated text.
Ranked #5 on Visual Storytelling on VIST
no code implementations • CVPR 2020 • Vedika Agarwal, Rakshith Shetty, Mario Fritz
Despite significant success in Visual Question Answering (VQA), VQA models have been shown to be notoriously brittle to linguistic variations in the questions.
no code implementations • CVPR 2019 • Rakshith Shetty, Bernt Schiele, Mario Fritz
Importance of visual context in scene understanding tasks is well recognized in the computer vision community.
no code implementations • 17 Dec 2018 • Rakshith Shetty, Bernt Schiele, Mario Fritz
We propose a method to quantify the sensitivity of black-box vision models to visual context by editing images to remove selected objects and measuring the response of the target models.
no code implementations • NeurIPS 2018 • Rakshith Shetty, Mario Fritz, Bernt Schiele
While great progress has been made recently in automatic image manipulation, it has been limited to object centric images like faces or structured scene datasets.
no code implementations • 6 Nov 2017 • Rakshith Shetty, Bernt Schiele, Mario Fritz
In this paper, we propose an automatic method, called Adversarial Author Attribute Anonymity Neural Translation ($A^4NT$), to combat such text-based adversaries.
2 code implementations • ICCV 2017 • Hamed R. -Tavakoli, Rakshith Shetty, Ali Borji, Jorma Laaksonen
To bridge the gap between humans and machines in image understanding and describing, we need further insight into how people describe a perceived scene.
3 code implementations • ICCV 2017 • Rakshith Shetty, Marcus Rohrbach, Lisa Anne Hendricks, Mario Fritz, Bernt Schiele
While strong progress has been made in image captioning over the last years, machine and human captions are still quite distinct.
1 code implementation • 17 Aug 2016 • Rakshith Shetty, Jorma Laaksonen
We present our submission to the Microsoft Video to Language Challenge of generating short captions describing videos in the challenge dataset.
2 code implementations • 9 Dec 2015 • Rakshith Shetty, Jorma Laaksonen
In this paper, we describe the system for generating textual descriptions of short video clips using recurrent neural networks (RNN), which we used while participating in the Large Scale Movie Description Challenge 2015 in ICCV 2015.