# Grounded Video Description

arXiv: https://arxiv.org/abs/1812.06587 · HTML: https://mlsanity.com/paper/1812.06587/

## Key facts

- Cited by 78 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2018.

## Built on by (most cited first, 30 of 78)

- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Ferret: Refer and Ground Anything Anywhere at Any Granularity](https://mlsanity.com/paper/2310.07704/) (2023)
- [Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning](https://mlsanity.com/paper/2302.14115/) (2023)
- [ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding](https://mlsanity.com/paper/2305.08275/) (2023)
- [End-to-End Dense Video Captioning with Parallel Decoding](https://mlsanity.com/paper/2108.07781/) (2021)
- [UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling](https://mlsanity.com/paper/2111.12085/) (2021)
- [Large Language Models are Few-Shot Health Learners](https://mlsanity.com/paper/2305.15525/) (2023)
- [Boximator: Generating Rich and Controllable Motions for Video Synthesis](https://mlsanity.com/paper/2402.01566/) (2024)
- [Language Models Can See: Plugging Visual Controls in Text Generation](https://mlsanity.com/paper/2205.02655/) (2022)
- [Self-Supervised Learning for Videos: A Survey](https://mlsanity.com/paper/2207.00419/) (2022)
- [Positive-Augmented Contrastive Learning for Image and Video Captioning Evaluation](https://mlsanity.com/paper/2303.12112/) (2023)
- [Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning](https://mlsanity.com/paper/2103.16564/) (2021)
- [VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos](https://mlsanity.com/paper/2411.04923/) (2024)
- [Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing](https://mlsanity.com/paper/2412.19806/) (2024)
- [EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching](https://mlsanity.com/paper/2111.08919/) (2021)
- [Semantic Grouping Network for Video Captioning](https://mlsanity.com/paper/2102.00831/) (2021)
- [Refer-it-in-RGBD: A Bottom-up Approach for 3D Visual Grounding in RGBD Images](https://mlsanity.com/paper/2103.07894/) (2021)
- [Visual Abductive Reasoning](https://mlsanity.com/paper/2203.14040/) (2022)
- [Coarse-Fine Networks for Temporal Activity Detection in Videos](https://mlsanity.com/paper/2103.01302/) (2021)
- [Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing](https://mlsanity.com/paper/2109.10540/) (2021)
- [Consensus Graph Representation Learning for Better Grounded Image Captioning](https://mlsanity.com/paper/2112.00974/) (2021)
- [Text with Knowledge Graph Augmented Transformer for Video Captioning](https://mlsanity.com/paper/2303.12423/) (2023)
- [A Review of Deep Learning for Video Captioning](https://mlsanity.com/paper/2304.11431/) (2023)
- [VLTinT: Visual-Linguistic Transformer-in-Transformer for Coherent Video Paragraph Captioning](https://mlsanity.com/paper/2211.15103/) (2022)
- [Connecting Vision and Language with Video Localized Narratives](https://mlsanity.com/paper/2302.11217/) (2023)
- [Towards Diverse Paragraph Captioning for Untrimmed Videos](https://mlsanity.com/paper/2105.14477/) (2021)
- [Vision+X: A Survey on Multimodal Learning in the Light of Data](https://mlsanity.com/paper/2210.02884/) (2022)
- [End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding](https://mlsanity.com/paper/2203.08013/) (2022)
- [VLCap: Vision-Language with Contrastive Learning for Coherent Video Paragraph Captioning](https://mlsanity.com/paper/2206.12972/) (2022)
- [Look at What I'm Doing: Self-Supervised Spatial Grounding of Narrations in Instructional Videos](https://mlsanity.com/paper/2110.10596/) (2021)

## Abstract (arXiv)

Video description is one of the most challenging problems in vision and language understanding due to the large variability both on the video and language side. Models, hence, typically shortcut the difficulty in recognition and generate plausible sentences that are based on priors but are not necessarily grounded in the video. In this work, we explicitly link the sentence to the evidence in the video by annotating each noun phrase in a sentence with the corresponding bounding box in one of the frames of a video. Our dataset, ActivityNet-Entities, augments the challenging ActivityNet Captions dataset with 158k bounding box annotations, each grounding a noun phrase. This allows training video description models with this data, and importantly, evaluate how grounded or "true" such model are to the video they describe. To generate grounded captions, we propose a novel video description model which is able to exploit these bounding box annotations. We demonstrate the effectiveness of our model on our dataset, but also show how it can be applied to image description on the Flickr30k Entities dataset. We achieve state-of-the-art performance on video description, video paragraph description, and image description and demonstrate our generated sentences are better grounded in the video.