Preserving Modes and Messages via Diverse Particle Selection
Authors: Jason Pacheco, Silvia Zuffi, Michael Black, Erik Sudderth
ICML 2014 | Conference PDF | Archive PDF | Plain Text | 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* PACHECOJ@CS.BROWN.EDU Department of Computer Science, Brown University, Providence, RI 02912, USA Silvia Zuffi* SILVIA.ZUFFI@TUE.MPG.DE Max Planck Institute for Intelligent Systems, 72076 T ubingen, Germany; ITC-CNR, 20133 Milan, Italy Michael J. Black BLACK@TUE.MPG.DE Max Planck Institute for Intelligent Systems, 72076 T ubingen, Germany Erik B. Sudderth SUDDERTH@CS.BROWN.EDU 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. |