Video Summarization: Towards Entity-Aware Captions
Not on the map. Why?
Abstract (arXiv)
Existing popular video captioning benchmarks and models deal with generic captions devoid of specific person, place or organization named entities. In contrast, news videos present a challenging setting where the caption requires such named entities for meaningful summarization. As such, we propose the task of summarizing news video directly to entity-aware captions. We also release a large-scale dataset, VIEWS (VIdeo NEWS), to support research on this task. Further, we propose a method that augments visual information from videos with context retrieved from external world knowledge to generate entity-aware captions. We demonstrate the effectiveness of our approach on three video captioning models. We also show that our approach generalizes to existing news image captions dataset. With all the extensive experiments and insights, we believe we establish a solid basis for future research on this challenging task.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 36 of its 54 references that are in the map. First posted on arXiv 1 Dec 2023.
Builds on
36 of 54 references.
References · 36
- 2017Attention Is All You Needcited by 59,932
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,337
- 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
- 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,027
- 2023Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understandingcited by 1,461
- 2023PaLM 2 Technical Reportcited by 1,154
- 2017Dense-Captioning Events in Videoscited by 789
24 more
- 2022PaLI: A Jointly-Scaled Multilingual Language-Image Modelcited by 762
- 2017Towards Automatic Learning of Procedures from Web Instructional Videoscited by 616
- 2022GIT: A Generative Image-to-text Transformer for Vision and Languagecited by 536
- 2023EVA-02: A Visual Representation for Neon Genesiscited by 377
- 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 296
- 2023Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioningcited by 263
- 2023PaLI-X: On Scaling up a Multilingual Vision and Language Modelcited by 254
- 2020TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrievalcited by 215
- 2016Movie Descriptioncited by 180
- 2023Unlimiformer: Long-Range Transformers with Unlimited Length Inputcited by 127
- 2022REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memorycited by 122
- 2020Visual News: Benchmark and Challenges in News Image Captioningcited by 121
- 2023Encyclopedic VQA: Visual questions about detailed properties of fine-grained categoriescited by 105
- 2020Condensed Movies: Story Based Retrieval with Contextual Embeddingscited by 95
- 2022Retrieval Augmented Visual Question Answering with Outside Knowledgecited by 93
- 2019Good News, Everyone! Context driven entity-aware captioning for news imagescited by 66
- 2020Transform and Tell: Entity-Aware News Image Captioningcited by 52
- 2020VMSMO: Learning to Generate Multimodal Summary for Video-based News Articlescited by 44
- 2015Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Datacited by 40
- 2018Entity-aware Image Caption Generationcited by 26
- 2022There is a Time and Place for Reasoning Beyond the Imagecited by 22
- 2019Informative Image Captioning with External Sources of Informationcited by 14
- 2022Summary-Oriented Vision Modeling for Multimodal Abstractive Summarizationcited by 14
- 2022Focus! Relevant and Sufficient Context Selection for News Image Captioningcited by 10
Built on by
Not cited yet.