Papers › PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding
PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding
Jang Hyun Cho, Andrea Madotto, Effrosyni Mavroudi, Triantafyllos Afouras, Tushar Nagarajan, Muhammad Maaz, Yale Song, Tengyu Ma, Shuming Hu, Suyog Jain, Miguel Martin, Huiyu Wang, Hanoona Rasheed, Peize Sun, Po-Yao Huang, Daniel Bolya, Nikhila Ravi, Shashank Jain, Tammy Stark, Shane Moon, Babak Damavandi, Vivian Lee, Andrew Westbury, Salman Khan, Philipp Krähenbühl, Piotr Dollár, Lorenzo Torresani, Kristen Grauman, Christoph Feichtenhofer
Vision-language models are integral to computer vision research, yet many high-performing 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 human-labeled 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 & models.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Question Answering | NExT-QA | PLM-8B | Accuracy | 84.1 | #4 of 47 | Archive leaderboard | report |
| Video Question Answering | NExT-QA | PLM-3B | Accuracy | 83.4 | #6 of 47 | Archive leaderboard | report |
| Video Question Answering | NExT-QA | PLM-1B | Accuracy | 80.3 | #12 of 47 | Archive leaderboard | report |
| Video Question Answering | TVBench | PLM-8B | Average Accuracy | 63.5 | #2 of 28 | Archive leaderboard | report |
| Video Question Answering | TVBench | PLM-3B | Average Accuracy | 58.9 | #5 of 28 | Archive leaderboard | report |
| Video Question Answering | TVBench | PLM-1B | Average Accuracy | 50.4 | #12 of 28 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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