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..
SpotEM: Efficient Video Search for Episodic Memory
Authors: Santhosh Kumar Ramakrishnan, Ziad Al-Halah, Kristen Grauman
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on 200+ hours of video from the Ego4D EM Natural Language Queries benchmark and three different EM models demonstrate the effectiveness of our approach: computing only 10% 25% of the clip features, we preserve 84% 97% of the original EM model s accuracy. |
| Researcher Affiliation | Collaboration | 1UT Austin 2University of Utah 3FAIR, Meta AI. Correspondence to: S. Ramakrishnan <EMAIL>. |
| Pseudocode | No | The paper does not contain explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project page: https:// vision.cs.utexas.edu/projects/spotem |
| Open Datasets | Yes | We evaluate our approach on the large-scale EM NLQ benchmark from Ego4D (Grauman et al., 2022), which is the only public dataset supporting this task to our knowledge. |
| Dataset Splits | Yes | The dataset contains 11.3k/3.9k/4.0k queries annotated over 136/45/46 hours of train/val/test videos. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments (e.g., specific GPU or CPU models). |
| Software Dependencies | No | The paper mentions using PyTorch for implementation but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | We provide the hyperparameters for training Spot EM in Table 3. |