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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Despite the great progress of Visual Question Answering (VQA), current VQAmodels heavily rely on the superficial correlation between the question typeand its corresponding frequent answers (i.e., language priors) to makepredictions, without really understanding the input. In this work, we definethe training instances with the same question type but different answers as\textit{superficially similar instances}, and attribute the language priors tothe confusion of VQA model on such instances. To solve this problem, we proposea novel training framework that explicitly encourages the VQA model todistinguish between the superficially similar instances. Specifically, for eachtraining instance, we first construct a set that contains its superficiallysimilar counterparts. Then we exploit the proposed distinguishing module toincrease the distance between the instance and its counterparts in the answerspace. In this way, the VQA model is forced to further focus on the other partsof the input beyond the question type, which helps to overcome the languagepriors. Experimental results show that our method achieves the state-of-the-artperformance on VQA-CP v2. Codes are available at\href{https://github.com/wyk-nku/Distinguishing-VQA.git}{Distinguishing-VQA}.

Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar Instances