AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Loss

• URL: http://arxiv.org/abs/2105.01993v1
• Date: Wed, 5 May 2021 11:41:38 GMT
• Title: AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Loss
• Authors: Yangyang Guo and Liqiang Nie and Zhiyong Cheng and Feng Ji and Ji Zhang and Alberto Del Bimbo
• Abstract summary: This work attempts to tackle the language prior problem from the viewpoint of the feature space learning. An adapted margin cosine loss is designed to discriminate the frequent and the sparse answer feature space. Experimental results demonstrate that our adapted margin cosine loss can greatly enhance the baseline models.
• Score: 73.65872901950135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A number of studies point out that current Visual Question Answering (VQA) models are severely affected by the language prior problem, which refers to blindly making predictions based on the language shortcut. Some efforts have been devoted to overcoming this issue with delicate models. However, there is no research to address it from the angle of the answer feature space learning, despite of the fact that existing VQA methods all cast VQA as a classification task. Inspired by this, in this work, we attempt to tackle the language prior problem from the viewpoint of the feature space learning. To this end, an adapted margin cosine loss is designed to discriminate the frequent and the sparse answer feature space under each question type properly. As a result, the limited patterns within the language modality are largely reduced, thereby less language priors would be introduced by our method. We apply this loss function to several baseline models and evaluate its effectiveness on two VQA-CP benchmarks. Experimental results demonstrate that our adapted margin cosine loss can greatly enhance the baseline models with an absolute performance gain of 15\% on average, strongly verifying the potential of tackling the language prior problem in VQA from the angle of the answer feature space learning.

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