Radial Loss for Learning Fine-grained Video Similarity Metric

Abhinav Jain; Prerna Agarwal; Shashank Mujumdar; Nitin Gupta; Sameep Mehta; Chiranjoy Chattopadhyay

ICASSP 2019accepted0 citations

Radial Loss for Learning Fine-grained Video Similarity Metric

Abhinav Jain, Prerna Agarwal, Shashank Mujumdar, Nitin Gupta, Sameep Mehta, Chiranjoy Chattopadhyay

Abstract

In this paper, we propose the Radial Loss which utilizes category and sub-category labels to learn an order-preserving fine-grained video similarity metric. We propose an end-to-end quadlet-based Convolutional Neural Network (CNN) combined with Long Short-term Memory (LSTM) Unit to model video similarities by learning the pairwise distance relationships between samples in a quadlet generated using the category and sub-category labels. We showcase two novel applications of learning a video similarity metric - (i) fine-grained video retrieval, (ii) fine-grained event detection, along with simultaneous shot boundary detection, and correspondingly show promising results against those of the baselines on two new fine-grained video datasets.

BibTeX

@inproceedings{icassp2019_radiallossforlea,
  title = {Radial Loss for Learning Fine-grained Video Similarity Metric},
  author = {Abhinav Jain and Prerna Agarwal and Shashank Mujumdar and Nitin Gupta and Sameep Mehta and Chiranjoy Chattopadhyay},
  booktitle = {ICASSP 2019},
  year = {2019}
}