The dense captioning task aims at detecting multiple salient regions of an image and describing them separately in natural language. Although significant advancements in the field of dense captioning have been made, there are still some limitations to existing methods in recent years. On the one hand, most dense captioning methods lack strong target detection capabilities and struggle to cover all relevant content when dealing with target-intensive images. On the other hand, current transformer-based methods are powerful but neglect the acquisition and utilization of contextual information, hindering the visual understanding of local areas. To address these issues, we propose a common and distinct knowledge-mining network with content interaction for the task of dense captioning. Our network has a knowledge mining mechanism that improves the detection of salient targets by capturing common and distinct knowledge from multi-scale features. We further propose a content interaction module that combines region features into a unique context based on their correlation. Our experiments on various benchmarks have shown that the proposed method outperforms the current state-of-the-art methods.

Accurately estimating the 3D pose and shape is an essential step towards understanding animal behavior, and can potentially benefit many downstream applications, such as wildlife conservation. However, research in this area is held back by the lack of a comprehensive and diverse dataset with high-quality 3D pose and shape annotations. In this paper, we propose Animal3D, the first comprehensive dataset for mammal animal 3D pose and shape estimation. Animal3D consists of 3379 images collected from 40 mammal species, high-quality annotations of 26 key-points, and importantly the pose and shape parameters of the SMAL [50] model. All annotations were labeled and checked manually in a multi-stage process to ensure highest quality results. Based on the Animal3D dataset, we benchmark representative shape and pose estimation models at: (1) supervised learning from only the Animal3D data, (2) synthetic to real transfer from synthetically generated images, and (3) fine-tuning human pose and shape estimation models. Our experimental results demonstrate that predicting the 3D shape and pose of animals across species remains a very challenging task, despite significant advances in human pose estimation. Our results further demonstrate that synthetic pre-training is a viable strategy to boost the model performance. Overall, Animal3D opens new directions for facilitating future research in animal 3D pose and shape estimation, and is publicly available.

Seeking online health information may reinforce the anxiety of those who are already overly anxious about their health. This study explored how people with health anxiety may behave differently in terms of their attentional biases when seeking health information online. We conducted an eye-tracking experiment with 17 participants in the high health anxious group and 17 participants in the low health anxious group, who performed three types of information-seeking tasks (factual, interpretive, and exploratory) on a Chinese health website. We observed that both groups mainly allocated their attention to the stages of evaluating the list of search results and synthesizing information to make health decisions. They showed similar attention tracks at the earlier search stages and health anxiety was found to associate with attentional biases towards certain website stimuli. However, the high health anxious group showed more active eye movements than their low health anxious counterparts. Attentional biases from the high health anxious group mainly occurred at the later stage of processing rather than the initial orientation stages. As for task types, the high health anxious group presented more extensive attentional biases when performing the interpretive task, compared to the explorative and factual tasks. The findings provide novel insights into the attentional biases of people with health anxiety as they search online for health information, which have implications on designing more effective information interventions for vulnerable groups of health information consumers. The findings can also help clinicians interpret patients’ anxiety-related sensations and provide intervening recommendations for clients in use of online health information.