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..
Incomplete Multi-Modal Visual Data Grouping
Authors: Handong Zhao, Hongfu Liu, Yun Fu
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, we extensively test our method and competitors on one synthetic data, two RGB-D video datasets and two image datasets. The superior results validate the benefits of the proposed method, especially when multimodal data suffer from large incompleteness. |
| Researcher Affiliation | Academia | Department of Electrical and Computer Engineering, Northeastern University, Boston, USA, 02115 College of Computer and Information Science, Northeastern University, Boston, USA, 02115 |
| Pseudocode | No | No explicit pseudocode or algorithm block is present in the provided text, although "Algorithm 1" is referenced in Section 2.4. |
| Open Source Code | No | No explicit statement regarding the release of source code or a link to a code repository was found. |
| Open Datasets | Yes | (a) MSR Action Pairs dataset [Oreifej and Liu, 2013]... (b) MSR Daily Activity dataset [Wang et al., 2012]... (c) BUAA Nir Vis [Huang et al., 2012]... (d) UCI handwritten digit1 consists of 0-9 handwritten digits data from UCI repository. It includes 2000 examples, with one modality being the 76 Fourier coefficients and modal-2 being the 240 pixel averages in 2 3 windows.1http://archive.ics.uci.edu/ml/datasets.html |
| Dataset Splits | No | The paper describes testing under different partial/incomplete example ratios (PER) but does not provide explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or processor types) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., library names with version numbers) required to replicate the experiments. |
| Experiment Setup | Yes | For the complete algorithm, we initialize the variables and parameters (in the iteration #0, denoted as (0) ) in ALM as follows: penalty parameter µ(0) = 10 3, = 1.1, the max penalty parameter µmax = 106, stopping threshold = 10 6, P(0) = Q(0) = Y(0) = 0 2 RN k, Pc(0) = Qc(0) = Yc(0) = 0 2 Rc k, ˆP (1)(0) = ˆQ(1)(0) = ˆY (1)0 2 Rm k, ˆP (2)(0) = ˆQ(2)(0) = ˆY (2)(0) = 0 2 Rn k, U (1)Rk d1, U (2)(0) = 0 2 Rk d2, A(0) = LA(0) = 0 2 RN N. |