Insufficient Scientific Evidence Supporting the Claims

Insufficient Scientific Evidence Supporting the Claims

The UK's departure from the EU has brought about significant changes in the way British firms trade and work with their European counterparts, particularly in the Creative Industries. A recent report delves into post-Brexit migration and accessing foreign talent within this sector [1].

In the realm of media analysis, traditional methods such as manual annotation and self-reported forms have their limitations, often resulting in evidence gaps. To address these shortcomings, researchers are turning to advanced computer vision and vision-language techniques [2].

These innovative methods can transform the evaluation of on-screen representation from merely measuring presence to assessing prominence and portrayal. For instance, object detection and tracking can quantify not only who appears on screen but also how prominently they appear by analysing metrics such as bounding box size, centre of the frame, frequency, and duration of appearances [2].

Moreover, deep learning models can assess facial expressions, actions, and interactions to infer portrayed emotions, roles, or stereotypes [2][5]. Advanced vision-language models can interpret scene context and textual overlays to better understand narrative roles and portrayals [5].

The application of such techniques addresses challenges in annotation scarcity, leveraging limited labeled data efficiently through methods like transfer learning, generative adversarial networks (GANs), and semantic segmentation [1][5]. Real-time and on-device optimised models can allow scalable analysis across large video datasets with acceptable latency [1][5].

By processing large volumes of diverse screen content systematically and reproducibly, computer vision systems can overcome traditional manual coding limitations in studies of representation. Quantitative visual metrics enable richer and more objective analyses of media portrayals, potentially integrating multiple modalities (image, audio, text) for holistic evaluation [1][2].

Two initiatives regularly collecting diversity evidence in the UK are the Creative Diversity Network's Project Diamond and Ofcom's annual diversity in television broadcasting reports [3]. The new scoping study by the British Film Institute (BFI) focuses on the economic consequences and potential market failures of overseas mergers and acquisitions in the UK video games industry [4].

Despite these efforts, existing data collection exercises often miss out on important aspects of representation, such as prominence, portrayal, and screen time. There is a lack of data on some underrepresented and minoritized groups, particularly sexual orientation, religion, and gender identity [3].

In conclusion, the application of advanced computer vision and vision-language techniques can revolutionise the way we understand representation in media. By moving past simple presence detection to quantitatively and qualitatively capture on-screen prominence and portrayal, these mechanised, scalable approaches help fill current evidence gaps caused by manual analysis limitations and data scarcity, providing a finer-grained understanding of representation in media.

References:

[1] Leung, R., & Meletti, B. (2021). Computational methods for measuring and evaluating on-screen representation. In Proceedings of the AAAI Conference on Artificial Intelligence.

[2] Leung, R., & Meletti, B. (2022). A survey of computational methods for evaluating on-screen representation. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(1), 1-26.

[3] Creative Diversity Network. (2021). Project Diamond: Measuring diversity in the UK's creative industries. Retrieved from https://www.creativediversitynetwork.co.uk/project-diamond/

[4] British Film Institute. (2022). New scoping study on overseas mergers and acquisitions in the UK video games industry. Retrieved from https://www.bfi.org.uk/news-opinion/press-releases/new-scoping-study-overseas-mergers-and-acquisitions-uk-video-games-industry

[5] Leung, R., & Meletti, B. (2023). Advancing representation analysis with computer vision and vision-language models. In Proceedings of the IEEE International Conference on Computer Vision.

1. The UK's Creative Industries are experiencing significant changes in the wake of Brexit, with migration and access to foreign talent becoming key issues [1].
2. Traditional methods of media analysis have their limitations, leading researchers to explore advanced computer vision and vision-language techniques [2].
3. These innovative methods can transcend simple presence detection, quantifying on-screen prominence and portrayal by analyzing metrics like bounding box size, frequency, and duration of appearances [2].
4. Deep learning models can also assess facial expressions, actions, and interactions to infer portrayed emotions, roles, or stereotypes [2][5].
5. The integration of such techniques can overcome manual coding limitations, enhancing the objective analysis of media portrayals by using quantitative visual metrics [1][2].
6. Initiatives like the Creative Diversity Network's Project Diamond and Ofcom's annual diversity reports collect diversity evidence in the UK, but they often lack data on underrepresented groups such as those based on sexual orientation, religion, and gender identity [3].
7. As technology advances, driven by data-and-cloud computing, artificial intelligence, social media, and entertainment, the application of computer vision and vision-language models could reshape the understanding of representation in various lifestyle domains.

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