Transductive Zero-Shot Learning with Visual Structure Constraint
Authors: Ziyu Wan, Dongdong Chen, Yan Li, Xingguang Yan, Junge Zhang, Yizhou Yu, Jing Liao
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on many widely used datasets demonstrate that the proposed visual structure constraint can bring substantial performance gain consistently and achieve state-of-the-art results. |
| Researcher Affiliation | Collaboration | 1 City University of Hong Kong 2 Microsoft Cloud+AI 3 PCG, Tencent 4 Shenzhen University 5 NLPR, CASIA 6 Deepwise AI Lab |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available at https://github.com/raywzy/VSC. |
| Open Datasets | Yes | extensive experiments are conducted on four widely-used ZSL benchmark datasets, i.e., Aw A1, Aw A2, CUB ,SUN10, and SUN72. Following the same configuration of previous methods, two different data split strategies are adopted: 1) Standard Splits (SS): The standard seen/unseen class split is first proposed in [17] and then widely used in most ZSL works. 2) Proposed Splits (PS): This split way is proposed by[32] to remove the overlapped Image Net-1K classes from target domain since it is used to pre-train the CNN model. |
| Dataset Splits | Yes | Following the same configuration of previous methods, two different data split strategies are adopted: 1) Standard Splits (SS): The standard seen/unseen class split is first proposed in [17] and then widely used in most ZSL works. 2) Proposed Splits (PS): This split way is proposed by[32] to remove the overlapped Image Net-1K classes from target domain since it is used to pre-train the CNN model. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' and 'pretrained Res Net-101' but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Using Adam optimizer, our method is trained for 5000 epochs with a fixed learning rate of 0.0001. The weight β in CDVSc and BMVSc is cross-validated in [10 4, 10 3] and [10 5, 10 4] respectively, while WDVSc directly sets β = 0.001 because of its very stable performance. All images are resized to 224 224 without any data augmentation, and the dimension of extracted features is 2048. The hidden unit numbers of the two FC layers in the embedding network are both 2048. Both visual features and semantic attributes are L2-normalized. |