Semantic understanding for contextual in-video advertising
Rishi Madhok, Shashank Mujumdar, et al.
AAAI 2018
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.
Rishi Madhok, Shashank Mujumdar, et al.
AAAI 2018
David Haws, Xiaodong Cui
ICASSP 2019
Nishtha Madaan, Shashank Mujumdar, et al.
SCC 2018
Jayachandu Bandlamudi, Kushal Mukherjee, et al.
IAAI 2023