CSK-SNIFFER: Commonsense Knowledge for Sniffing Object Detection Errors

Anurag Garg, Niket Tandon, Aparna S. Varde

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


This paper showcases the demonstration of a system called CSK-SNIFFER to automatically predict failures of an object detection model on images in big data sets from a target domain by identifying errors based on commonsense knowledge. CSK-SNIFFER can be an assistant to a human (as sniffer dogs are assistants to police searching for problems at airports). To cut through the clutter after deployment, this “sniffer” identifies where a given model is probably wrong. Alerted thus, users can visually explore within our demo, the model’s explanation based on spatial correlations that make no sense. In other words, it is impossible for a human without the help of a sniffer to flag false positives in such large data sets without knowing ground truth (unknown earlier since it is found after deployment). CSK-SNIFFER spans human-AI collaboration. The AI role is harnessed via embedding commonsense knowledge in the system; while an important human part is played by domain experts providing labeled data for training (besides human commonsense deployed by AI). Another highly significant aspect is that the human-in-the-loop can improve the AI system by the feedback it receives from visualizing object detection errors, while the AI provides actual assistance to the human in object detection. CSK-SNIFFER exemplifies visualization in big data analytics through spatial commonsense and a visually rich demo with numerous complex images from target domains. This paper provides excerpts of the CSK-SNIFFER system demo with a synopsis of its approach and experiments.

Original languageEnglish
JournalCEUR Workshop Proceedings
StatePublished - 2022
Event2022 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2022 - Edinburgh, United Kingdom
Duration: 29 Mar 2022 → …


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