{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/look-deeper-see-richer-depth-aware-image","title":"Look Deeper See Richer: Depth-aware Image Paragraph Captioning","arxiv_id":null,"date":"2018-10-15","proceeding":"ACM International Conference on Multimedia 2018 10","authors":["Ziwei Wang","Yadan Luo","Yang Li","Zi Huang","Hongzhi Yin"],"abstract":"With the widespread availability of image captioning at a sentence level, how to automatically generate image paragraphs is yet well explored. Describing an image by a full paragraph involves organising sentences orderly, coherently and diversely, inevitably leading higher complexity than by a single sentence. Existing image paragraph captioning methods give a series of sentences to represent the objects and regions of interests, where the descriptions are essentially generated by feeding the image fragments containing objects and regions into conventional image single-sentence captioning models. This strategy is difficult to generate the descriptions that guarantee the stereoscopic hierarchy and non-overlapping objects. In this paper, we propose a Depth-aware Attention Model (\\textitDAM ) to generate paragraph captions for images. The depths of image areas are firstly estimated in order to discriminate objects in a range of spatial locations, which can further guide the linguistic decoder to reveal spatial relationships among objects. This model completes the paragraph in a logical and coherent manner. By incorporating the attention mechanism, the learned model swiftly shifts the sentence focus during paragraph generation, whilst avoiding verbose descriptions on a same object. Extensive quantitative experiments and the user study have been conducted on the Visual Genome dataset, which demonstrate the effectiveness and the interpretability of the proposed model.","url_abs":"https://dl.acm.org/doi/abs/10.1145/3240508.3240583","url_pdf":"https://dl.acm.org/doi/pdf/10.1145/3240508.3240583","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"image-captioning","task_name":"Image Captioning"},{"task_slug":"image-paragraph-captioning","task_name":"Image Paragraph Captioning"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-paragraph-captioning-on-image-paragraph","task":"Image Paragraph Captioning","dataset":"Image Paragraph Captioning","model":"Depth-aware Attention Model (DAM)","rank_in_archive_order":9,"of":10,"metrics":{"BLEU-4":"6.7","CIDEr":"17.3","METEOR":"13.9"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}