Caught Red-Handed: Toward Practical Video-Based Subsequences Matching in the Presence of Real-World Transformations

Xu, Yi; Price, True; Monrose, Fabian; Frahm, Jan‐Michael

doi:10.1109/cvprw.2017.182

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…The dramatic rise of pirated content has been fueled by the large amounts of user-generated content (UGC) and professionally-generated content (PGC) uploaded to content sharing market, e.g., over 500 hours of video are uploaded to YouTube every minute [1] , the average monthly paying users of Bilibili have increased by 62% in one year alone [2]. These videos can generate significant advertising revenue, providing strong incentive for unscrupulous individuals that wish to capitalize on this bonanza by skillful copyright infringement [3]. Some video editing specialists even design methods to evade infringement detection algorithms by cropping, melting, and merging short clips from popular videos which make accurate copy detection even more challenging.…”

Section: Introductionmentioning

confidence: 99%

A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection

He¹,

Yang²,

Jiang³

et al. 2022

Preprint

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In this paper, we introduce VCSL (Video Copy Segment Localization), a new comprehensive segment-level annotated video copy dataset. Compared with existing copy detection datasets restricted by either video-level annotation or small-scale, VCSL not only has two orders of magnitude more segment-level labelled data, with 160k realistic video copy pairs containing more than 280k localized copied segment pairs, but also covers a variety of video categories and a wide range of video duration. All the copied segments inside each collected video pair are manually extracted and accompanied by precisely annotated starting and ending timestamps. Alongside the dataset, we also propose a novel evaluation protocol that better measures the prediction accuracy of copy overlapping segments between a video pair and shows improved adaptability in different scenarios. By benchmarking several baseline and state-of-the-art segment-level video copy detection methods with the proposed dataset and evaluation metric, we provide a comprehensive analysis that uncovers the strengths and weaknesses of current approaches, hoping to open up promising directions for future works. The VCSL dataset, metric and benchmark codes are all publicly available at https://github.com/alipay/VCSL.

show abstract

Section: Introductionmentioning

confidence: 99%

A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection

He¹,

Yang²,

Jiang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This translates to about 3 years of non-stop watching in order to consume all videos uploaded to YouTube in a single hour. Similarly, Instagram 2 users post nearly 70 million photos and videos each day (Xu et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Movie genome: alleviating new item cold start in movie recommendation

Deldjoo

Dacrema

Constantin

et al. 2019

User Model User-Adap Inter

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As of today, most movie recommendation services base their recommendations on collaborative filtering (CF) and/or content-based filtering (CBF) models that use metadata (e.g., genre or cast). In most video-on-demand and streaming services, however, new movies and TV series are continuously added. CF models are unable to make predictions in such a scenario, since the newly added videos lack interactions-a problem technically known as new item cold start (CS). Currently, the most common approach to this problem is to switch to a purely CBF method, usually by exploiting textual metadata. This approach is known to have lower accuracy than CF because it ignores useful collaborative information and relies on human-generated textual metadata, which are expensive to collect and often prone to errors. User-generated content, such as tags, can also be rare or absent in CS situations. In this paper, we introduce a new movie recommender system that addresses the new item problem in the movie domain by (i) integrating state-of-the-art audio and visual descriptors, which can be automatically extracted from video content and constitute what we call the movie genome; (ii) exploiting an effective data fusion method named canonical correlation analysis, which was successfully tested in our previous works Deldjoo et alto better exploit complementary information between different modalities; (iii) proposing a two-step hybrid approach which trains a CF model on warm items (items with interactions) and leverages the learned model on the movie genome to recommend cold items (items without interactions). Experimental validation is carried out using a system-centric study on a large-scale, real-world movie recommendation dataset both in an absolute cold start and in a cold to warm transition; and a user-centric online experiment measuring different subjective aspects, such as satisfaction and diversity. Results show the benefits of this approach compared to existing approaches.

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The complementarity of a diverse range of deep learning features extracted from video content for video recommendation

Almeida

Villiers

Freitas

et al. 2022

Expert Systems with Applications

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Caught Red-Handed: Toward Practical Video-Based Subsequences Matching in the Presence of Real-World Transformations

Cited by 4 publications

References 26 publications

A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection

A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection

Movie genome: alleviating new item cold start in movie recommendation

The complementarity of a diverse range of deep learning features extracted from video content for video recommendation

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