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..
Multi-View Priors for Learning Detectors from Sparse Viewpoint Data
Authors: Bojan Pepik; Michael Stark; Peter Gehler; Bernt Schiele
ICLR 2014 | Venue PDF | 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. |