Somandepalli, Krishna; Hebbar, Rajat; Narayanan, Shrikanth
Robust Character Labeling in Movie Videos: Data Resources and Self-supervised Feature Adaptation. Journal Article
In: IEEE Transactions on Multimedia, 24 , pp. 3355 - 3368, 2021.
Abstract | Links | BibTeX | Tags: computational media understanding, face clustering, face diarization, multiview correlation, self-supervision, triplet loss, video character labeling
@article{Somandepalli2021b,
title = {Robust Character Labeling in Movie Videos: Data Resources and Self-supervised Feature Adaptation.},
author = {Krishna Somandepalli and Rajat Hebbar and Shrikanth Narayanan},
url = {https://sail.usc.edu/publications/files/Somandepalli-TMM2021.pdf},
doi = {10.1109/TMM.2021.3096155},
year = {2021},
date = {2021-07-09},
urldate = {2021-07-09},
journal = {IEEE Transactions on Multimedia},
volume = {24},
pages = {3355 - 3368},
abstract = {Robust face clustering is a vital step in enabling computational understanding of visual character portrayal in media. Face clustering for long-form content is challenging because of variations in appearance and lack of supporting large-scale labeled data. Our work in this paper focuses on two key aspects of this problem: the lack of domain-specific training or benchmark datasets, and adapting face embeddings learned on web images to long-form content, specifically movies. First, we present a dataset of over 169000 face tracks curated from 240 Hollywood movies with weak labels on whether a pair of face tracks belong to the same or a different character. We propose an offline algorithm based on nearest-neighbor search in the embedding space to mine hard-examples from these tracks. We then investigate triplet-loss and multiview correlation-based methods for adapting face embeddings to hard-examples. Our experimental results highlight the usefulness of weakly labeled data for domain-specific feature adaptation. Overall, we find that multiview correlation-based adaptation yields more discriminative and robust face embeddings. Its performance on downstream face verification and clustering tasks is comparable to that of the state-of-the-art results in this domain. We also present the SAIL-Movie Character Benchmark corpus developed to augment existing benchmarks. It consists of racially diverse actors and provides face-quality labels for subsequent error analysis. We hope that the large-scale datasets developed in this work can further advance automatic character labeling in videos. All resources are available freely at https://sail.usc.edu/~ccmi/multiface .
},
keywords = {computational media understanding, face clustering, face diarization, multiview correlation, self-supervision, triplet loss, video character labeling},
pubstate = {published},
tppubtype = {article}
}