Abstract
Dense Video Object Captioning (DVOC) is the task of jointly detecting, tracking, and captioning object trajectories in a video, requiring the ability to understand spatio-temporal details and describe them in natural language. Due to the complexity of the task and the high cost associated with manual annotation, previous approaches resort to disjoint training strategies, potentially leading to suboptimal performance. To circumvent this issue, we propose to generate captions about spatio-temporally localized entities leveraging a state-of-the-art VLM. By extending the LVIS and LV-VIS datasets with our synthetic captions (LVISCap and LV-VISCap), we train MaskCaptioner, an end-to-end model capable of jointly detecting, segmenting, tracking and captioning object trajectories. Moreover, with pretraining on LVISCap and LV-VISCap, MaskCaptioner achieves state-of-the-art DVOC results on three existing benchmarks, VidSTG, VLN and BenSMOT.
Annotation pipeline
Our MaskCaptioner data annotation pipeline. We use this pipeline to generate the LVISCap and LV-VISCap datasets, which serve to train MaskCaptioner. To generate object-level captions, we feed both a visual prompt and a text prompt to a VLM (Gemini 2.0 flash).
Some synthetic annotations examples from our proposed LV-VICap dataset.
MaskCaptioner Architecture
Overview of our MaskCaptioner architecture. Videos are divided into clips that are processed sequentially in a semi-online manner. After generating queries and predictions for each object within a clip, tracking and captioning can be performed at the video level.
Comparison with State Of The Art
MaskCaptioner achieves state of the art DVOC results on the VidSTG, VLN and BenSMOT benchmarks with pre-training on our LVIScap and LV-VIScap datasets.
BibTeX
@inproceedings{fiastre2025maskcaptionerlearningjointly},
title={MaskCaptioner : Learning to Jointly Segment and Caption Object Trajectories in Videos},
author={Gabriel Fiastre and Antoine Yang and Cordelia Schmid},
year={2025},
eprint={2510.14904},
arxivePrefix{arXiv},
url={https://arxiv.org/abs/2510.14904}
}