PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding

Vision-language models are integral to computer vision research, yet many highperforming models remain closed-source, obscuring their data, design and training recipe. The research community has responded by using distillation from black-box models to label training data, achieving strong benchmark results, at the cost of measurable scientific progress. However, without knowing the details of the teacher model and its data sources, scientific progress remains difficult to measure. In this paper, we study building a Perception Language Model (PLM) in a fully open and reproducible framework for transparent research in image and video understanding. We analyze standard training pipelines without distillation from proprietary models and explore large-scale synthetic data to identify critical data gaps, particularly in detailed video understanding. To bridge these gaps, we release 2.8M humanlabeled instances of fine-grained video question-answer pairs and spatio-temporally grounded video captions. Additionally, we introduce PLM-VideoBench, a suite for evaluating challenging video understanding tasks focusing on the ability to reason about "what", "where", "when", and "how" of a video. We make our work fully reproducible by providing data, training recipes, code and models at https://github.com/facebookresearch/perception_models.

We examine scaling properties of our synthetic data under controlled setup and establish scaling laws. 10 12 25 30 35 40 45 50 55 Avg. Error No Syth. (1B) No Syth. (3B) No Syth. (8B) Video QA Err. = (2.5e+03 x GFLOPs) 0.15 1B 3B 8B 10 12 GFLOPs No Syth. (1B) No Syth. (3B) No Syth. (8B) OCR QA Err. = (6.5e+03 x GFLOPs) 0.20 10 12