Incomplete Multi-Modal Visual Data Grouping
Authors: Handong Zhao, Hongfu Liu, Yun Fu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |