176 citations · 211 across the 45 of their papers we have counts for
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Failure or Drift? Evaluating Monocular SLAM under Synthetic and Real-World Corruptions
Abhay Skaria Thomas, Shashank Agnihotri, Margret Keuper
Visual SLAM is commonly evaluated on clean trajectories, although deployment failures are often caused by adverse weather, illumination, blur, and sensor artifacts. Controlled corr…
RAWDet-7: A Multi-Scenario Benchmark for Object Detection and Description on Quantized RAW Images
Mishal Fatima, Shashank Agnihotri, Kanchana Vaishnavi Gandikota +2
Most vision models are trained on RGB images processed through ISP pipelines optimized for human perception, which can discard sensor-level information useful for machine reasoning…
-Quant: Towards Learnable Quantization for Low-bit Pattern Recognition
Mishal Fatima, Shashank Agnihotri, Marius Bock +4
Most pattern recognition models are developed on pre-proce\-ssed data. In computer vision, for instance, RGB images processed through image signal processing (ISP) pipelines design…
Improved Robustness from Biologically Inspired Sparse Contrast Representations
Lorena Stracke, Lia Nimmermann, Shashank Agnihotri +3
Deep neural networks surpass humans on many vision benchmarks, yet remain far less robust to distribution shifts such as illumination and weather changes. Existing approaches addre…
Deepfakes: we need to re-think the concept of "real" images
Janis Keuper, Margret Keuper
The wide availability and low usability barrier of modern image generation models has triggered the reasonable fear of criminal misconduct and negative social implications. The mac…
AIM: Amending Inherent Interpretability via Self-Supervised Masking
Eyad Alshami, Shashank Agnihotri, Bernt Schiele +1
It has been observed that deep neural networks (DNNs) often use both genuine as well as spurious features. In this work, we propose "Amending Inherent Interpretability via Self-Sup…