100 citations · 219 across the 10 of their papers we have counts for
10 papers
Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection
Farzaneh Rezaeianaran, Rakshith Shetty, Rahaf Aljundi +3
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…
Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing
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. Due to deficiencies…
Not Using the Car to See the Sidewalk: Quantifying and Controlling the Effects of Context in Classification and Segmentation
Rakshith Shetty, Bernt Schiele, Mario Fritz
Importance of visual context in scene understanding tasks is well recognized in the computer vision community. However, to what extent the computer vision models for image classifi…
Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions
M. Wagner, H. Basevi, R. Shetty +4
In-depth scene descriptions and question answering tasks have greatly increased the scope of today's definition of scene understanding. While such tasks are in principle open ended…
Adversarial Scene Editing: Automatic Object Removal from Weak Supervision
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. In this work, we…
: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation
Rakshith Shetty, Bernt Schiele, Mario Fritz
Text-based analysis methods allow to reveal privacy relevant author attributes such as gender, age and identify of the text's author. Such methods can compromise the privacy of an…
Paying Attention to Descriptions Generated by Image Captioning Models
Hamed R. Tavakoli, Rakshith Shetty, Ali Borji +1
To bridge the gap between humans and machines in image understanding and describing, we need further insight into how people describe a perceived scene. In this paper, we study the…
Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training
Rakshith Shetty, Marcus Rohrbach, Lisa Anne Hendricks +2
While strong progress has been made in image captioning over the last years, machine and human captions are still quite distinct. A closer look reveals that this is due to the defi…
Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption Generation
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. Our model is based on the encoder--d…
Video captioning with recurrent networks based on frame- and video-level features and visual content classification
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…