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 [1].
Continual Compositional Zero-Shot Learning
Authors: Yang Zhang, Songhe Feng, Jiazheng Yuan
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We design the CCZSL evaluation protocol and conduct extensive experiments on widely used benchmarks, demonstrating the superiority of our method compared to the state-of-the-art CZSL methods. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China 2Tangshan Research Institute, Beijing Jiaotong University, Beijing, China 3College of Science and Technology, Beijing Open University, Beijing, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 The optimization procedure of CCZSL. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We conduct experiments on two widely adopted datasets in CZSL, which are UT-Zappos [Yu and Grauman, 2014] and C-GQA [Naeem et al., 2021]. |
| Dataset Splits | Yes | Specifically, We split UT-Zappos into 3 sessions and C-GQA into 6 sessions. Details of splits are present in Table 2, including number of new attributes, new objects, new training compositions, new validation compositions and new testing compositions in each session. |
| Hardware Specification | No | The paper mentions using a ResNet-18 pre-trained on ImageNet but does not specify any hardware used for training or inference (e.g., GPU model, CPU type). |
| Software Dependencies | No | The paper mentions using the Adam optimizer, but it does not specify any software libraries or their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The number of super-primitives K is set to 4 for UT-Zappos and 20 for C-GQA. The original primitive embeddings are learned from scratch. For all datasets, we train the model using Adam optimizer with a learning rate of 5e-5. The temperature factor is 0.05 for all datasets. |