Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Grounded Video Situation Recognition
Authors: Zeeshan Khan, C.V. Jawahar, Makarand Tapaswi
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | When evaluated on a grounding-augmented version of the Vid Situ dataset, we observe a large improvement in entity captioning accuracy, as well as the ability to localize verb-roles without grounding annotations at training time. 4 Experiments We evaluate our model in two main settings. |
| Researcher Affiliation | Academia | Zeeshan Khan C. V. Jawahar Makarand Tapaswi CVIT, IIIT Hyderabad |
| Pseudocode | No | The paper describes the model architecture and steps in natural language and with diagrams, but does not include formal pseudocode or algorithm blocks. |
| Open Source Code | No | More examples on our project page, https://zeeshank95.github.io/grvidsitu/GVSR.html. |
| Open Datasets | Yes | We evaluate our model on the Vid Situ [27] dataset that consists of 29k videos (23.6k train, 1.3k val, and others in task-specific test sets) collected from a diverse set of 3k movies. |
| Dataset Splits | Yes | We evaluate our model on the Vid Situ [27] dataset that consists of 29k videos (23.6k train, 1.3k val, and others in task-specific test sets) collected from a diverse set of 3k movies. |
| Hardware Specification | Yes | As we use pretrained features, we train our model on a single RTX-2080 GPU, batch size of 16. |
| Software Dependencies | No | We implement our model in Pytorch [22]. We use the Adam optimizer [12] with a learning rate of 10^-4 to train the whole model end-to-end. |
| Experiment Setup | Yes | All the three Transformers have the same configurations they have 3 layers with 8 attention heads, and hidden dimension 1024. We use the Adam optimizer [12] with a learning rate of 10^-4 to train the whole model end-to-end. As we use pretrained features, we train our model on a single RTX-2080 GPU, batch size of 16. |