Deep Semantic Structural Constraints for Zero-Shot Learning
Authors: Yan Li, Zhen Jia, Junge Zhang, Kaiqi Huang, Tieniu Tan
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments Datasets We evaluate the DSSC model and compare with existing state-of-the-art approaches on three standard zero-shot learning benchmark datasets. Table 1 summarizes their statistics. Animals with Attributes (Aw A) (Lampert, Nickisch, and Harmeling 2014) includes 30,475 images from 50 animals categories. We adopt the class-level continuous 85-dim attributes as the semantic representations and use the standard 40/10 zero-shot split. |
| Researcher Affiliation | Academia | Yan Li, 1,2 Zhen Jia, 1,2 Junge Zhang,1,2 Kaiqi Huang,1,2,3 Tieniu Tan1,2,3 1 CRIPAC & NLPR, Institute of Automation, Chinese Academy of Sciences 2 University of Chinese Academy of Sciences 3 CAS Center for Excellence in Brain Science and Intelligence Technology |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Animals with Attributes (Aw A) (Lampert, Nickisch, and Harmeling 2014) includes 30,475 images from 50 animals categories. Caltech-UCSD Birds 200-2011 (CUB) (Wah et al. 2011) is a fine-grained bird dataset with 200 different species of birds and 11,788 images. SUN-Attribute (SUN) (Patterson et al. 2014) contains 14,340 images coming from 717 fine-grained scenes. |
| Dataset Splits | No | The paper specifies training and test class splits for datasets (e.g., '40/10 zero-shot split' for Aw A, '150/50 zero-shot split' for CUB, '645 classes for training and 72 classes for test' for SUN), but it does not explicitly define a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions models like 'Goog Le Net' and 'Res Net-101' and datasets like 'Image Net' and 'MIT Places dataset', but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | LIF Sc = max(0, m IF Sc + d(φ(xi), φ(xk)) d(φ(xi), φ(xj))) where ... m IF Sc is the margin of the IFSc and is set to 1.0 for all experiments. LDSSC = Lsoftmax + λLIF Sc + βLSESc where λ and β are trivially set to 1.0 for all the experiments. During the training stage, all images are resized to 256 256 pixels. |