With the advancement of vision-language models, image captioning has made significant progress, leading to the generation of more accurate and detailed descriptions. Current image captioning primarily focuses on describing the apparent visual characteristics, which are easily observed by most humans, but less helpful in real-world scenarios. When users seek a deeper understanding of visual content, they may be concerned with fine-grained categories, function properties, and other background knowledge, rather than merely appearances. Additionally, as users' interests vary, there is a growing demand for customizable content generation. To address these challenges, we propose the task of image narrative generation, which aims to produce knowledge-rich natural language responses for input images, customized to the user preference. Furthermore, we propose T^4, an image narrative generation model progressing through cascade steps: Tailor, reTrieve, Think, and Tell. Specifically, it takes the image and various types of prompts as input, and first refines or predicts potentially interesting queries that are tailored to the user expertise level. Subsequently, the model enriches contextual knowledge through retrieval-augmentation and employs chain-of-thoughts to decompose the generation process step by step, thereby telling an accurate and logically coherent image narrative. In addition, we construct the ImgNarr-23K dataset to support task training and evaluation. Experimental results demonstrate that the proposed approach generates image narratives that better satisfy user requirements, and achieves state-of-the-art performance in knowledge-based VQA tasks without additional finetuning. T^4 presents a promising solution for customized content generation in specialized domains.

Image caption evaluation is a crucial task, which involves the semantic perception and matching of image and text. Good evaluation metrics aim to be fair, comprehensive, and consistent with human judge intentions. When humans evaluate a caption, they usually consider multiple aspects, such as whether it is related to the target image without distortion, how much image gist it conveys, as well as how fluent and beautiful the language and wording is. The above three different evaluation orientations can be summarized as fidelity, adequacy, and fluency. The former two rely on the image content, while fluency is purely related to linguistics and more subjective. Inspired by human judges, we propose a learning-based metric named FAIEr to ensure evaluating the fidelity and adequacy of the captions. Since image captioning involves two different modalities, we employ the scene graph as a bridge between them to represent both images and captions. FAIEr mainly regards the visual scene graph as the criterion to measure the fidelity. Then for evaluating the adequacy of the candidate caption, it high-lights the image gist on the visual scene graph under the guidance of the reference captions. Comprehensive experimental results show that FAIEr has high consistency with human judgment as well as high stability, low reference dependency, and the capability of reference-free evaluation.

Image-text retrieval of natural scenes has been a popular research topic. Since image and text are heterogeneous cross-modal data, one of the key challenges is how to learn comprehensive yet unified representations to express the multi-modal data. A natural scene image mainly involves two kinds of visual concepts, objects and their relationships, which are equally essential to image-text retrieval. Therefore, a good representation should account for both of them. In the light of recent success of scene graph in many CV and NLP tasks for describing complex natural scenes, we propose to represent image and text with two kinds of scene graphs: visual scene graph (VSG) and textual scene graph (TSG), each of which is exploited to jointly characterize objects and relationships in the corresponding modality. The image-text retrieval task is then naturally formulated as cross-modal scene graph matching. Specifically, we design two particular scene graph encoders in our model for VSG and TSG, which can refine the representation of each node on the graph by aggregating neighborhood information. As a result, both object-level and relationship-level cross-modal features can be obtained, which favorably enables us to evaluate the similarity of image and text in the two levels in a more plausible way. We achieve state-of-the-art results on Flickr30k and MS COCO, which verifies the advantages of our graph matching based approach for image-text retrieval.