English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In recent years, there has been a growing emphasis on the intersection ofaudio, vision, and text modalities, driving forward the advancements inmultimodal research. However, strong bias that exists in any modality can leadto the model neglecting the others. Consequently, the model's ability toeffectively reason across these diverse modalities is compromised, impedingfurther advancement. In this paper, we meticulously review each question typefrom the original dataset, selecting those with pronounced answer biases. Tocounter these biases, we gather complementary videos and questions, ensuringthat no answers have outstanding skewed distribution. In particular, for binaryquestions, we strive to ensure that both answers are almost uniformly spreadwithin each question category. As a result, we construct a new dataset, namedMUSIC-AVQA v2.0, which is more challenging and we believe could better fosterthe progress of AVQA task. Furthermore, we present a novel baseline model thatdelves deeper into the audio-visual-text interrelation. On MUSIC-AVQA v2.0,this model surpasses all the existing benchmarks, improving accuracy by 2% onMUSIC-AVQA v2.0, setting a new state-of-the-art performance.

Tackling Data Bias in MUSIC-AVQA: Crafting a Balanced Dataset for Unbiased Question-Answering