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20182021
most citedNot Using the Car to See the Sidewalk: Quantifying and Controlling the Effects of Context in Classification and Segmentation

3 citations · 3 across the 2 of their papers we have counts for

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cs.CV2021

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…

cs.CV2019

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…

cs.CV20183 cited

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…

cs.CV2018

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…

cs.CV2018

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…