Incomplete Attribute Learning with auxiliary labels
Authors: Kongming Liang, Yuhong Guo, Hong Chang, Xilin Chen
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on three widely used attribute prediction datasets. The experimental results show that our proposed method can achieve the state-of-the-art performance with access to partially observed attribute annotations. |
| Researcher Affiliation | Academia | Kongming Liang1,2,3, Yuhong Guo2, Hong Chang1, Xilin Chen1,3 1Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China 2School of Computer Science, Carleton University, Ottawa, Canada 3University of Chinese Academy of Sciences, Beijing 100049, China |
| Pseudocode | Yes | Algorithm 1 Optimization procedure |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the authors' own code for the described methodology. |
| Open Datasets | Yes | a Pascal [Farhadi et al., 2009] contains 6430 training images and 6355 testing images from Pascal VOC 2008 challenge. a Yahoo [Farhadi et al., 2009] contains 2644 images belonging to twelve object categories. INA (Image Net Attributes [Russakovsky and Fei-Fei, 2010]) contains 9,600 images across 384 categories. |
| Dataset Splits | Yes | For the a Pascal dataset, we use the default {train, test} split and separate half the training data for validation. For a Yahoo and INA, we randomly split the dataset into three subsets with equal size for training, validating and testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Convolutional Neural Networks (CNN) [Donahue et al., 2014] to extract 4096 De CAF features' and 'Alex Net [Donahue et al., 2014] and VGG-16 [Simonyan and Zisserman, 2014]' as base networks, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For our proposed approach, we select the trade-off parameters α from {10 2; 10 1; 1; 10; 100}, select β from {10 5; 10 4; 10 3; 10 2} while setting β and γ to be equal. |