Finding Frequent Entities in Continuous Data
Authors: Ferran Alet, Rohan Chitnis, Leslie P. Kaelbling, Tomas Lozano-Perez
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We go on to describe the algorithm and its formal guarantees and describe experiments that find the main characters in video of a TV show and that address the household object-finding problem. |
| Researcher Affiliation | Academia | Ferran Alet, Rohan Chitnis, Leslie P. Kaelbling and Tom as Lozano-P erez MIT Computer Science and Artificial Intelligence Laboratory {alet, ronuchit, lpk, tlp}@mit.edu |
| Pseudocode | Yes | Algorithm 1: Hop And Count with multiple radii. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology (HAC). |
| Open Datasets | Yes | We created a dataset of 8 humans moving objects around 20 different locations in a room.1 You can find it at http://lis.csail.mit.edu/alet/entities.html |
| Dataset Splits | No | The paper mentions using "70% of the data" for sampling in the House M.D. experiment, but does not specify clear train/validation/test splits for model training or evaluation. For the object localization experiment, no explicit splits are mentioned. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions using dlib, sklearn, Sharp Mask, and Inception-V3, but does not specify version numbers for these software dependencies. |
| Experiment Setup | Yes | We run HAC with r = 0.5 and apply duplicate reduction with rd = 0.65. These parameters were not fine-tuned; they were picked based on comments from the paper that created the CNN and on figure 6. We fix ϵ = δ = 0.5 for all experiments; these large values are sufficient because HAC works better in high dimensions than guaranteed by theorem 2.1. Run HAC with τl = (all points have the same weight regardless of their time), τs = 10 seconds, f = 2.5% and a distance function and threshold which link two detections that happen roughly within 30 centimeters and have features that are close in embedding space. |