We demonstrate a video captioning bot, named Seeing Bot, which can generate a natural language description about what it is seeing in near real time. Specifcally, given a live streaming video, Seeing Bot runs two pre-learned and complementary captioning modules in parallel\u2014one for generating image-level caption for each sampled frame, and the other for generating video-level caption for each sampled video clip. In particular, both the image and video captioning modules are boosted by incorporating semantic attributes which can enrich the generated descriptions, leading to human-level caption generation. A visual-semantic embedding model is then exploited to rank and select the final caption from the two parallel modules by considering the semantic relevance between video content and the generated captions. The Seeing Bot finally converts the generated description to speech and sends the speech to an end user via an earphone. Our demonstration is conducted on any videos in the wild and supports live video captioning.<\/p>\n","protected":false},"excerpt":{"rendered":"
We demonstrate a video captioning bot, named Seeing Bot, which can generate a natural language description about what it is seeing in near real time. Specifcally, given a live streaming video, Seeing Bot runs two pre-learned and complementary captioning modules in parallel\u2014one for generating image-level caption for each sampled frame, and the other for generating […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13562],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-394163","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-computer-vision","msr-locale-en_us"],"msr_publishername":"International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)","msr_edition":"","msr_affiliation":"","msr_published_date":"2017-08-08","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"4","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"394166","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"dp18-panA","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2017\/06\/dp18-panA.pdf","id":394166,"label_id":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Yingwei Pan","user_id":0,"rest_url":false},{"type":"text","value":"Zhaofan Qiu","user_id":0,"rest_url":false},{"type":"user_nicename","value":"tiyao","user_id":34069,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=tiyao"},{"type":"text","value":"Houqiang Li","user_id":0,"rest_url":false},{"type":"user_nicename","value":"tmei","user_id":34188,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=tmei"}],"msr_impact_theme":[],"msr_research_lab":[199560],"msr_event":[],"msr_group":[144916],"msr_project":[212087],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":212087,"post_title":"Vision and Language","post_name":"video-and-language","post_type":"msr-project","post_date":"2016-01-14 20:03:50","post_modified":"2021-05-13 02:20:33","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/video-and-language\/","post_excerpt":"Automatically describing visual content with natural language is a fundamental challenge of computer vision and multimedia. Sequence learning (e.g., Recurrent Neural Networks), attention mechanism, memory networks,\u00a0etc.,\u00a0have attracted increasing attention on visual interpretation. In this project, we are focusing on the following topics related to the emerging topic of \"vision and language\": Image and video captioning, including MSR-VTT video to language grand challenge and datasets (http:\/\/ms-multimedia-challenge.com\/). 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