Visual and semantic saliency are important in image captioning. However, single-phase image captioning benefits little from limited saliency without a saliency predictor. In this paper, a novel saliency-enhanced re-captioning framework via two-phase learning is proposed to enhance the single-phase image captioning. In the framework, visual saliency and semantic saliency are distilled from the first-phase model and fused with the second-phase model for model self-boosting. The visual saliency mechanism can generate a saliency map and a saliency mask for an image without learning a saliency map predictor. The semantic saliency mechanism sheds some lights on the properties of words with part-of-speech Noun in a caption. Besides, another type of saliency, sample saliency is proposed to explicitly compute the saliency degree of each sample, which helps for more robust image captioning. In addition, how to combine the above three types of saliency for further performance boost is also examined. Our framework can treat an image captioning model as a saliency extractor, which may benefit other captioning models and related tasks. The experimental results on both the Flickr30k and MSCOCO datasets show that the saliency-enhanced models can obtain promising performance gains.

Visual impairments and blindness are major public health concerns globally. Effective eye disease screening aided by artificial intelligence (AI) is a promising countermeasure, although it is challenged by practical constraints such as poor image quality in community screening. The recently developed ophthalmic foundation model RETFound has shown higher accuracy in retinal image recognition tasks. This study developed an RETFound-enhanced deep learning (DL) model for multiple-eye disease screening using real-world images from community screenings. Our results revealed that our DL model improved the sensitivity and specificity by over 15% compared with commercial models. Our model also shows better generalisation ability than AI models developed using traditional processes. Additionally, decision curve analysis underscores the higher net benefit of employing our model in both urban and rural settings in China. These findings indicate that the RETFound-enhanced DL model can achieve a higher net benefit in community-based screening, advocating its adoption in low- and middle-income countries to address global eye health challenges.

Recent neural models for video captioning usually employ an attention-based encoder-decoder framework. However, current approaches mainly attend to the motion features and object features of the video when generating the caption, but ignore the potential but useful historical information. Besides, exposure bias and vanishing gradients problems always exist in current caption generation models. In this paper, we propose a novel video captioning framework, named Stacked Multimodal Attention Network (SMAN). It adopts additional visual and textual historical information during caption generation as context features, employs a stacked architecture to process different features gradually, and utilizes the Reinforcement Learning method and coarse-to-fine training strategy to further improve the generated results. Both quantitative and qualitative experiments on the benchmark datasets of MSVD and MSR-VTT show the effectiveness and feasibility of our framework. The codes are available on https://github.com/zhengyi123456/SMAN .

Bowen Zhao, Changkai Ji, Yuejie Zhang, Wen He, Yingwen Wang, Qing Wang, Rui Feng, Xiaobo Zhang. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.