(Deep) Learning from Frames

Wehrmann, Jônatas; Barros, Rodrigo C.; Simões, Gabriel; Paula, Thomas S.; Ruiz, Duncan D.

doi:10.1109/bracis.2016.012

Cited by 24 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More recent work has utilised deep learning and convolutional neural networks for genre classification. Wehrmann & Barros [44,45] used convolutions to learn the spatial as well as temporal characteristic-based relationships of the entire movie trailer, studying both audio and video features. Shambharkar et al [39] introduced a new video feature as well as three new audio features which proved useful in classifying genre, combining a CNN with audio features to provide promising results.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Rethinking movie genre classification with fine-grained semantic clustering

Fish¹,

Weinbren²,

Gilbert³

2020

Preprint

View full text Add to dashboard Cite

Movie genre classification is an active research area in machine learning. However, due to the limited labels available, there can be large semantic variations between movies within a single genre definition. We expand these 'coarse' genre labels by identifying 'fine-grained' semantic information within the multi-modal content of movies. By leveraging pre-trained 'expert' networks, we learn the influence of different combinations of modes for multi-label genre classification. Using a contrastive loss, we continue to fine-tune this 'coarse' genre classification network to identify high-level intertextual similarities between the films across all genre labels. This leads to a more 'fine-grained' and detailed clustering, based on semantic similarities while still retaining some genre information. Our approach is demonstrated on a newly introduced multi-modal 37,866,450 frame, 8,800 movie trailer dataset, MMX-Trailer-20, which includes precomputed audio, location, motion, and image embeddings.

show abstract

Section: Related Workmentioning

confidence: 99%

“…In [45] it's shown how capturing temporal information using both 3D convolutions and LSTM's can help with the genre classification task. To see if temporal information could be retained through concatenation of scenes and sequences, we experimented with a number of scene length variations.…”

Section: Effect Of Sequence Lengthmentioning

confidence: 99%

Rethinking movie genre classification with fine-grained semantic clustering

Fish¹,

Weinbren²,

Gilbert³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…More recent work has utilised deep learning and convolutional neural networks for genre classification. Wehrmann & Barros [28,29] used convolutions to learn the spatial as well as temporal characteristic-based relationships of the entire movie trailer, studying both audio and video features. Shambharkar et al [25] introduced a new video feature and three new audio features that proved useful in classifying genre, combining a CNN with audio features to provide promising results.…”

Section: Introductionmentioning

confidence: 99%

Rethinking Genre Classification With Fine Grained Semantic Clustering

Fish

Gilbert

Weinbren

2021

2021 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

Movie genre classification is an active research area in machine learning; however, the content of movies can vary widely within a single genre label. We expand these 'coarse' genre labels by identifying 'fine-grained' contextual relationships within the multi-modal content of videos. By leveraging pretrained 'expert' networks, we learn the influence of different combinations of modes for multi-label genre classification. Then, we continue to fine-tune this 'coarse' genre classification network self-supervised to sub-divide the genres based on the multi-modal content of the videos. Our approach is demonstrated on a new multi-modal 37,866,450 frame, 8,800 movie trailer dataset, MMX-Trailer-20, which includes pre-computed audio, location, motion, and image embeddings.

show abstract

Movie Genre Filtering for Automated Parental Control

Yong

Hoo

2020

Intelligent Robotics and Applications

View full text Add to dashboard Cite

(Deep) Learning from Frames

Cited by 24 publications

References 18 publications

Rethinking movie genre classification with fine-grained semantic clustering

Rethinking movie genre classification with fine-grained semantic clustering

Rethinking Genre Classification With Fine Grained Semantic Clustering

Movie Genre Filtering for Automated Parental Control

Contact Info

Product

Resources

About