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..
Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network
Authors: Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify the effectiveness of our tracking algorithm through extensive experiment on a challenging benchmark, where our method illustrates outstanding performance compared to the state-of-the-art tracking algorithms. This section describes our implementation details and experimental setting. The effectiveness of our tracking algorithm is then demonstrated by quantitative and qualitative analysis on a large number of benchmark sequences. |
| Researcher Affiliation | Collaboration | Seunghoon Hong1 EMAIL Tackgeun You1 EMAIL Suha Kwak2 EMAIL Bohyung Han1 EMAIL 1Dept. of Computer Science and Engineering, POSTECH, Pohang, Korea 2Inria WILLOW Project, Paris, France |
| Pseudocode | No | The paper describes its algorithm in prose and uses mathematical equations, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a link or statement confirming the availability of its own source code for the methodology described. It mentions using 'Caffe: An open source convolutional architecture' and using 'available source code to reproduce the results' from other papers, but not its own. |
| Open Datasets | Yes | To evaluate the performance, we employ all 50 sequences from the recently released tracking benchmark dataset (Wu et al., 2013). |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits (e.g., percentages or counts). It describes how training examples for the online SVM are generated during tracking but not a predefined dataset partitioning for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU or CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using the 'Caffe library (Jia, 2013)' but does not provide a specific version number for it or any other software dependencies. |
| Experiment Setup | Yes | The CNN takes an image from sample bounding box, which is resized to 227 227, and outputs a 4096-dimensional vector from its first fullyconnected (fc6) layer as a feature vector corresponding to the sample. To generate target candidates in each frame, we draw N(= 120) samples... the threshold δ in Eq. (16) is set to 0.3. The number of observations m used to build generative model in Eq. (13) is set to 30. All parameters are fixed for all sequences throughout our experiment. |