# Context and Attribute Grounded Dense Captioning

arXiv: https://arxiv.org/abs/1904.01410 · HTML: https://mlsanity.com/paper/1904.01410/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 30 of 31)

- [Interactive and Explainable Region-guided Radiology Report Generation](https://mlsanity.com/paper/2304.08295/) (2023)
- [From Show to Tell: A Survey on Deep Learning-based Image Captioning](https://mlsanity.com/paper/2107.06912/) (2021)
- [DRAMA: Joint Risk Localization and Captioning in Driving](https://mlsanity.com/paper/2209.10767/) (2022)
- [GRiT: A Generative Region-to-text Transformer for Object Understanding](https://mlsanity.com/paper/2212.00280/) (2022)
- [Caption Anything: Interactive Image Description with Diverse Multimodal Controls](https://mlsanity.com/paper/2305.02677/) (2023)
- [Language Models Can See: Plugging Visual Controls in Text Generation](https://mlsanity.com/paper/2205.02655/) (2022)
- [Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning](https://mlsanity.com/paper/2309.06597/) (2023)
- [Toward Visual Grounding: A Survey](https://mlsanity.com/paper/2412.20206/) (2024)
- [DetCLIPv3: Towards Versatile Generative Open-vocabulary Object Detection](https://mlsanity.com/paper/2404.09216/) (2024)
- [Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds](https://mlsanity.com/paper/2204.10688/) (2022)
- [LocCa: Visual Pretraining with Location-aware Captioners](https://mlsanity.com/paper/2403.19596/) (2024)
- [Segment and Caption Anything](https://mlsanity.com/paper/2312.00869/) (2023)
- [CapDet: Unifying Dense Captioning and Open-World Detection Pretraining](https://mlsanity.com/paper/2303.02489/) (2023)
- [IC3: Image Captioning by Committee Consensus](https://mlsanity.com/paper/2302.01328/) (2023)
- [Contextual Modeling for 3D Dense Captioning on Point Clouds](https://mlsanity.com/paper/2210.03925/) (2022)
- [Pixel Aligned Language Models](https://mlsanity.com/paper/2312.09237/) (2023)
- [ControlCap: Controllable Region-level Captioning](https://mlsanity.com/paper/2401.17910/) (2024)
- [Dense Video Object Captioning from Disjoint Supervision](https://mlsanity.com/paper/2306.11729/) (2023)
- [A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing Objects in 3D Scenes](https://mlsanity.com/paper/2403.07469/) (2024)
- [Similar Scenes arouse Similar Emotions: Parallel Data Augmentation for Stylized Image Captioning](https://mlsanity.com/paper/2108.11912/) (2021)
- [FlexCap: Describe Anything in Images in Controllable Detail](https://mlsanity.com/paper/2403.12026/) (2024)
- [Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner](https://mlsanity.com/paper/2305.11769/) (2023)
- [DynRefer: Delving into Region-level Multimodal Tasks via Dynamic Resolution](https://mlsanity.com/paper/2405.16071/) (2024)
- [Spatial Preference Rewarding for MLLMs Spatial Understanding](https://mlsanity.com/paper/2510.14374/) (2025)
- [WeedVision: Multi-Stage Growth and Classification of Weeds using DETR and RetinaNet for Precision Agriculture](https://mlsanity.com/paper/2502.14890/) (2025)
- [One Patch to Caption Them All: A Unified Zero-Shot Captioning Framework](https://mlsanity.com/paper/2510.02898/) (2025)
- [Surveying the Landscape of Image Captioning Evaluation: A Comprehensive Taxonomy, Trends and Metrics Analysis](https://mlsanity.com/paper/2408.04909/) (2024)
- [Describing image focused in cognitive and visual details for visually impaired people: An approach to generating inclusive paragraphs](https://mlsanity.com/paper/2202.05331/) (2022)
- [Bypass Network for Semantics Driven Image Paragraph Captioning](https://mlsanity.com/paper/2206.10059/) (2022)
- [CapHDR2IR: Caption-Driven Transfer from Visible Light to Infrared Domain](https://mlsanity.com/paper/2411.16327/) (2024)

## Abstract (arXiv)

Dense captioning aims at simultaneously localizing semantic regions and describing these regions-of-interest (ROIs) with short phrases or sentences in natural language. Previous studies have shown remarkable progresses, but they are often vulnerable to the aperture problem that a caption generated by the features inside one ROI lacks contextual coherence with its surrounding context in the input image. In this work, we investigate contextual reasoning based on multi-scale message propagations from the neighboring contents to the target ROIs. To this end, we design a novel end-to-end context and attribute grounded dense captioning framework consisting of 1) a contextual visual mining module and 2) a multi-level attribute grounded description generation module. Knowing that captions often co-occur with the linguistic attributes (such as who, what and where), we also incorporate an auxiliary supervision from hierarchical linguistic attributes to augment the distinctiveness of the learned captions. Extensive experiments and ablation studies on Visual Genome dataset demonstrate the superiority of the proposed model in comparison to state-of-the-art methods.