The Elephant in the Room
Not on the map. Why?
Abstract (arXiv)
We showcase a family of common failures of state-of-the art object detectors. These are obtained by replacing image sub-regions by another sub-image that contains a trained object. We call this "object transplanting". Modifying an image in this manner is shown to have a non-local impact on object detection. Slight changes in object position can affect its identity according to an object detector as well as that of other objects in the image. We provide some analysis and suggest possible reasons for the reported phenomena.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Aug 2018.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 1.
4 papers.
- 2024Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotationscited by 30
- 2024Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environmentcited by 12
- 2023Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systemscited by 2
- 2025Bridging the Gap Between Multimodal Foundation Models and World Modelscited by 1
- 2025Bridging the Gap Between Multimodal Foundation Models and World Models4 Oct 2025
- 2024Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment19 Jun 2024
- 2024Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations12 Mar 2024
- 2023Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems13 Nov 2023