Meta-learning and other approaches to few-shot learning are widely studied for image recognition, and are increasingly applied to other vision tasks such as pose estimation and dense prediction. This naturally raises the question of whether there is any fewshot metalearning algorithm capable of generalizing across these diverse task types? To support the community in answering this question, we introduce Meta Omnium, a dataset-of-datasets spanning multiple vision tasks including recognition, keypoint localization, semantic segmentation and regression. We experiment with popular fewshot metalearning baselines and analyze their ability to generalize across tasks and to transfer knowledge between them. Meta Omnium enables metalearning researchers to evaluate model generalization to a much wider array of tasks than previously possible, and provides a single framework for evaluating meta-learners across a wide suite of vision applications in a consistent manner. Code and dataset are available at https://github.com/edi-meta-learning/meta-omnium.

Medical Visual Question Answering as a specific-domain task requires substantive prior knowledge of medicine. However, deep learning techniques encounter severe problems of limited supervision due to the scarcity of well-annotated large-scale medical VQA datasets. As an alternative to facing the data limitation problem, image captioning can be introduced to learn summary information about the picture, which is beneficial to question answering. To this end, we propose a caption-aware VQA method that can read the summary information of image content and clinic diagnoses from plenty of medical images and answer the medical question with richer multimodality features. The proposed method consists of two novel components emphasizing semantic locations and semantic content respectively. Firstly, to extract and leverage the semantic locations implied in image captioning, similarity analysis is designed to summarize the attention maps generated from image captioning by their relevance and guide the visual model to focus on the semantic-rich regions. Besides, to combine the semantic content in the generated captions, we propose a Progressive Compact Bilinear Interactions structure to achieve cross-modality comprehension over the image, question and caption features by performing bilinear attention in a gradual manner. Qualitative and quantitative experiments on various medical datasets exhibit the superiority of the proposed approach compared to the state-of-the-art methods.

Medical images contain various abnormal regions, most of which are closely related to the lesions or diseases. The abnormality or lesion is one of the major concerns during clinical practice and therefore becomes the key in answering questions about medical images. However, the recent efforts still focus on constructing a generic Visual Question Answering framework for medical-domain tasks, which is not adequate for practical medical requirements and applications. In this paper, we present two novel medical-specific modules named multiplication anomaly sensitive module and residual anomaly sensitive module to utilize weakly supervised anomaly localization information in medical Visual Question Answering. Firstly, the proposed multiplication anomaly sensitive module designed for anomaly-related questions can mask the feature of the whole image according to the anomaly location map. Secondly, the residual anomaly sensitive module could learn a flexible anomaly feature while preserving the information of the original questioned image, which is more helpful in answering anomaly-unrelated questions. Thirdly, the transformer decoder and multi-task learning strategy are combined to further enhance the question-reasoning ability and the model generalization performance. Finally, qualitative and quantitative experiments on a variety of medical datasets exhibit the superiority of the proposed approaches compared to the state-of-the-art methods.

Anatomical landmark localization has been a key challenge for medical image analysis. Existing researches mostly adopt CNN as the main architecture for landmark localization while they are not applicable to process image modalities with periodic structure. In this paper, we propose a novel two-stage frame-level detection and heatmap regression model for accurate landmark localization in m-mode echocardiography, which promotes better integration between global context information and local appearance. Specifically, a periodic frame detection module with LSTM is designed to model periodic context and detect frames of systole and diastole from original echocardiography. Next, a CNN based heatmap regression model is introduced to predict landmark localization in each systolic or diastolic local region. Experiment results show that the proposed model achieves average distance error of 9.31, which is at a reduction by 24% comparing to baseline models.