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..
Learning to Detect Concepts from Webly-Labeled Video Data
Authors: Junwei Liang, Lu Jiang, Deyu Meng, Alexander Hauptmann
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The efficacy and the scalability of WELL have been extensively demonstrated on two public benchmarks, including the largest multimedia dataset and the largest manually-labeled video set. Experimental results show that WELL significantly outperforms the state-of-the-art methods. |
| Researcher Affiliation | Academia | Junwei Liang1, Lu Jiang1, Deyu Meng2, Alexander Hauptmann1 1School of Computer Science, Carnegie Mellon University, PA, USA 2School of Mathematics and Statistics, Xi an Jiaotong University, P. R. China. |
| Pseudocode | Yes | Algorithm 1: Webly-labeled Learning (WELL). |
| Open Source Code | No | The paper does not include any statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | The efficacy and the scalability of WELL have been extensively demonstrated on two public benchmarks, including by far the largest manually-labeled video set called FCVID [Jiang et al., 2015d] and the largest multimedia dataset called YFCC100M [Thomee et al., 2015]. |
| Dataset Splits | Yes | The exact stopping iteration for each detector is automatically tuned in terms of its performance on a small validation set. On FCVID, the set is a small training subset with manual labels whereas on YFCC100M it is a proportion of noisy training set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU models, CPU types, memory). |
| Software Dependencies | No | The paper mentions using Convolutional Neural Network (CNN) features and Lucene for indexing, but it does not specify any software names with version numbers required to replicate the experiments. |
| Experiment Setup | Yes | The concept detectors are trained based on a hinge loss cost function. Algorithm 1 is used to train the concept models iteratively and the λ stops increasing after 100 iterations. ... The hyper-parameters of all methods including the baseline methods are tuned on the same validation set. |