Fast Training of Pose Detectors in the Fourier Domain
Authors: João F. Henriques, Pedro Martins, Rui F Caseiro, Jorge Batista
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate the generality of the proposed model, we conducted object detection and pose estimation experiments on 3 widely different settings, which will be described shortly. ... For the performance evaluation, ground truth objects are assigned to hypothesis by the widely used Pascal criterion of bounding box overlap [7]. We then measure average precision (AP) and pose error... |
| Researcher Affiliation | Academia | Jo ao F. Henriques Pedro Martins Rui Caseiro Jorge Batista Institute of Systems and Robotics University of Coimbra {henriques,pedromartins,ruicaseiro,batista}@isr.uc.pt |
| Pseudocode | Yes | We give a Matlab implementation in Appendix B, which can use any off-the-shelf SVR solver without modification. |
| Open Source Code | Yes | The supplemental material is available at: www.isr.uc.pt/ henriques/transformations/ |
| Open Datasets | Yes | Our first test will be on a car detection task on satellite imagery [12]... We used TUD-Campus for training and TUD-Crossing for testing (see Fig. 2)... We chose the very recent KITTI benchmark [9]... |
| Dataset Splits | Yes | The first 7 annotated images were used for training, and the remaining 8 for validation. |
| Hardware Specification | No | The paper states "we report timings for single-core implementations" but does not specify any particular hardware components like CPU or GPU models, or memory details. |
| Software Dependencies | No | The paper mentions "Matlab implementation" and "off-the-shelf SVR solver" but does not provide specific version numbers for these software components. |
| Experiment Setup | No | The paper mentions implementing a detector based on HOG templates and training with RR and SVR, but it does not specify concrete experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or specific optimizer settings. |