Contrastive Personalization Approach to Suspect Identification (Student Abstract)

Authors

  • Devansh Gupta MIDAS Lab, Indraprastha Institute of Information Technology, New Delhi, India
  • Drishti Bhasin MIDAS Lab, Indraprastha Institute of Information Technology, New Delhi, India
  • Sarthak Bhagat MIDAS Lab, Indraprastha Institute of Information Technology, New Delhi, India
  • Shagun Uppal MIDAS Lab, Indraprastha Institute of Information Technology, New Delhi, India
  • Ponnurangam Kumaraguru International Institute of Information Technology, Hyderabad, India
  • Rajiv Ratn Shah MIDAS Lab, Indraprastha Institute of Information Technology, New Delhi, India

DOI:

https://doi.org/10.1609/aaai.v36i11.21617

Keywords:

Personalized Recommendation Systems, Relevance Feedback, Contrastive Learning

Abstract

Targeted image retrieval has long been a challenging problem since each person has a different perception of different features leading to inconsistency among users in describing the details of a particular image. Due to this, each user needs a system personalized according to the way they have structured the image in their mind. One important application of this task is suspect identification in forensic investigations where a witness needs to identify the suspect from an existing criminal database. Existing methods require the attributes for each image or suffer from poor latency during training and inference. We propose a new approach to tackle this problem through explicit relevance feedback by introducing a novel loss function and a corresponding scoring function. For this, we leverage contrastive learning on the user feedback to generate the next set of suggested images while improving the level of personalization with each user feedback iteration.
AAAI-22 Proceedings Cover

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Published

2022-06-28

How to Cite

Gupta, D., Bhasin, D., Bhagat, S., Uppal, S., Kumaraguru, P., & Shah, R. R. (2022). Contrastive Personalization Approach to Suspect Identification (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12961-12962. https://doi.org/10.1609/aaai.v36i11.21617

Issue

Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program