Multi-View Priors for Learning Detectors from Sparse Viewpoint Data
Authors: Bojan Pepik; Michael Stark; Peter Gehler; Bernt Schiele
ICLR 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As we show in our experiments, this transfer is not only beneficial for detectors based on basic-level category representations, but also enables the robust learning of detectors that represent classes at finer levels of granularity, where training data is typically even scarcer and more unbalanced. As a result, we report largely improved performance in simultaneous 2D object localization and viewpoint estimation on a recent dataset of challenging street scenes. |
| Researcher Affiliation | Academia | Bojan Pepik1 Michael Stark1,2 Peter Gehler3 Bernt Schiele1 1Max Planck Institute for Informatics, 2Stanford University, 3Max Planck Institute for Intelligent Systems |
| Pseudocode | No | The paper describes mathematical formulations and algorithmic steps in prose but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present structured steps in a code-like format. |
| Open Source Code | No | The paper states that 'The annotations will be made publicly available upon publication' which refers to data, but it does not provide an explicit statement or link for the open-sourcing of the code for the methodology described in the paper. |
| Open Datasets | Yes | We perform the analysis on two tasks, 2D bounding box localization and viewpoint estimation on the 3D Object Classes dataset [39] (a widely accepted multiview-benchmark with balanced training and test data from 8 viewpoint bins, for 9 object classes)... Specifically, we focus on the car object class on the KITTI street scene dataset [17] (and the tracking benchmark subset)... |
| Dataset Splits | No | The paper describes training and test sets and mentions using 'k training examples per view' and 'bootstrapping' for training source models, but it does not explicitly specify a distinct validation dataset split with percentages or counts for hyperparameter tuning. |
| Hardware Specification | No | The paper mentions 'For computational reasons, we restrict ourselves to the root-template-only version of the DPM', but it does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'DPM' and an 'SVM solver' but does not provide specific version numbers for these or any other software dependencies needed to replicate the experiments. |
| Experiment Setup | Yes | In all cases, the C parameter is fixed to 0.002 [12] for all tested methods. We set λ = 0.9/emax, where emax is the biggest eigenvalue of Σs. |