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..
Preserving Modes and Messages via Diverse Particle Selection
Authors: Jason Pacheco, Silvia Zuffi, Michael Black, Erik Sudderth
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 4 provides an extensive validation on the challenging problem of articulated human pose estimation from single images, demonstrating state-of-the-art performance and significant improvements over other particle max-product algorithms. |
| Researcher Affiliation | Academia | Jason Pacheco* EMAIL Department of Computer Science, Brown University, Providence, RI 02912, USA Silvia Zuffi* EMAIL Max Planck Institute for Intelligent Systems, 72076 T ubingen, Germany; ITC-CNR, 20133 Milan, Italy Michael J. Black EMAIL Max Planck Institute for Intelligent Systems, 72076 T ubingen, Germany Erik B. Sudderth EMAIL Department of Computer Science, Brown University, Providence, RI 02912, USA |
| Pseudocode | No | The paper includes flowcharts in Figure 1 illustrating the algorithms but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide a link to a code repository for the described methodology. |
| Open Datasets | Yes | We demonstrate the robustness of our proposed algorithm on the Buffy the Vampire Slayer dataset (Ferrari et al., 2008), a widely used benchmark for evaluating pose estimation methods based on part-based models. |
| Dataset Splits | Yes | The dataset consists of a standard partition of 276 test images and about 500 training images. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions general software components like 'SVM classifier' and 'logistic regression', but does not list any specific software dependencies or libraries with version numbers. |
| Experiment Setup | Yes | For all methods we use 200 particles and run for 300 iterations. |