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Julio C. S. Jacques Junior; Agata Lapedriza; Cristina Palmero; Xavier Baro; Sergio Escalera |
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Person Perception Biases Exposed: Revisiting the First Impressions Dataset |
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2021 |
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IEEE Winter Conference on Applications of Computer Vision |
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13-21 |
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This work revisits the ChaLearn First Impressions database, annotated for personality perception using pairwise comparisons via crowdsourcing. We analyse for the first time the original pairwise annotations, and reveal existing person perception biases associated to perceived attributes like gender, ethnicity, age and face attractiveness.
We show how person perception bias can influence data labelling of a subjective task, which has received little attention from the computer vision and machine learning communities by now. We further show that the mechanism used to convert pairwise annotations to continuous values may magnify the biases if no special treatment is considered. The findings of this study are relevant for the computer vision community that is still creating new datasets on subjective tasks, and using them for practical applications, ignoring these perceptual biases. |
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Virtual; January 2021 |
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WACV |
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HUPBA |
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no |
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Admin @ si @ JLP2021 |
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3533 |
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Author |
Minesh Mathew; Dimosthenis Karatzas; C.V. Jawahar |
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Title |
DocVQA: A Dataset for VQA on Document Images |
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Conference Article |
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2021 |
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IEEE Winter Conference on Applications of Computer Vision |
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2200-2209 |
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We present a new dataset for Visual Question Answering (VQA) on document images called DocVQA. The dataset consists of 50,000 questions defined on 12,000+ document images. Detailed analysis of the dataset in comparison with similar datasets for VQA and reading comprehension is presented. We report several baseline results by adopting existing VQA and reading comprehension models. Although the existing models perform reasonably well on certain types of questions, there is large performance gap compared to human performance (94.36% accuracy). The models need to improve specifically on questions where understanding structure of the document is crucial. The dataset, code and leaderboard are available at docvqa. org |
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Virtual; January 2021 |
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DAG; 600.121 |
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Admin @ si @ MKJ2021 |
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3498 |
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