Medical visual question answering (Med-VQA) aims to accurately answer a clinical question presented with a medical image. Despite its enormous potential in healthcare industry and services, the technology is still in its infancy and is far from practical use. Med-VQA tasks are highly challenging due to the massive diversity of clinical questions and the disparity of required visual reasoning skills for different types of questions. In this paper, we propose a novel conditional reasoning framework for Med-VQA, aiming to automatically learn effective reasoning skills for various Med-VQA tasks. Particularly, we develop a question-conditioned reasoning module to guide the importance selection over multimodal fusion features. Considering the different nature of closed-ended and open-ended Med-VQA tasks, we further propose a type-conditioned reasoning module to learn a different set of reasoning skills for the two types of tasks separately. Our conditional reasoning framework can be easily applied to existing Med-VQA systems to bring performance gains. In the experiments, we build our system on top of a recent state-of-the-art Med-VQA model and evaluate it on the VQA-RAD benchmark [23]. Remarkably, our system achieves significantly increased accuracy in predicting answers to both closed-ended and open-ended questions, especially for open-ended questions, where a 10.8% increase in absolute accuracy is obtained. The source code can be downloaded from https://github.com/awenbocc/med-vqa. CCS CONCEPTS • Computing methodologies → Computer vision tasks.

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This paper investigates the effectiveness of pre-training for few-shot intent classification. While existing paradigms commonly further pre-train language models such as BERT on a vast amount of unlabeled corpus, we find it highly effective and efficient to simply finetune BERT with a small set of labeled utterances from public datasets. Specifically, fine-tuning BERT with roughly 1,000 labeled data yields a pre-trained model -IntentBERT, which can easily surpass the performance of existing pre-trained models for few-shot intent classification on novel domains with very different semantics. The high effectiveness of IntentBERT confirms the feasibility and practicality of few-shot intent detection, and its high generalization ability across different domains suggests that intent classification tasks may share a similar underlying structure, which can be efficiently learned from a small set of labeled data. The source code can be found at https://github.com/ hdzhang-code/IntentBERT.

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Out-of-scope intent detection is of practical importance in task-oriented dialogue systems. Since the distribution of outlier utterances is arbitrary and unknown in the training stage, existing methods commonly rely on strong assumptions on data distribution such as mixture of Gaussians to make inference, resulting in either complex multi-step training procedures or hand-crafted rules such as confidence threshold selection for outlier detection. In this paper, we propose a simple yet effective method to train an out-of-scope intent classifier in a fully end-to-end manner by simulating the test scenario in training, which requires no assumption on data distribution and no additional postprocessing or threshold setting. Specifically, we construct a set of pseudo outliers in the training stage, by generating synthetic outliers using inliner features via self-supervision and sampling out-of-scope sentences from easily available open-domain datasets. The pseudo outliers are used to train a discriminative classifier that can be directly applied to and generalize well on the test task. We evaluate our method extensively on four benchmark dialogue datasets and observe significant improvements over state-of-the-art approaches. Our code has been released at https:// github.com/liam0949/DCLOOS.