Dialog-based Interactive Image Retrieval
Authors: Xiaoxiao Guo, Hui Wu, Yu Cheng, Steven Rennie, Gerald Tesauro, Rogerio Feris
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both simulated and real-world data show that 1) our proposed learning framework achieves better accuracy than other supervised and reinforcement learning baselines and 2) user feedback based on natural language rather than pre-specified attributes leads to more effective retrieval results, and a more natural and expressive communication interface. |
| Researcher Affiliation | Industry | Xiaoxiao Guo IBM Research AI xiaoxiao.guo@ibm.com Hui Wu IBM Research AI wuhu@us.ibm.com Yu Cheng IBM Research AI chengyu@us.ibm.com Steven Rennie Fusemachines Inc. srennie@gmail.com Gerald Tesauro IBM Research AI gtesauro@us.ibm.com Rogerio Schmidt Feris IBM Research AI rsferis@us.ibm.com |
| Pseudocode | No | The paper describes the model architecture and training process in detail but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'The project website is at: www.spacewu.com/posts/fashion-retrieval/' in a footnote. This is a project website, not an explicit statement that the source code for the methodology is openly available or a direct link to a code repository (e.g., GitHub). |
| Open Datasets | Yes | All experiments were performed on the Shoes dataset [53], with the same training and testing data split for all retrieval methods and for training the user simulator. |
| Dataset Splits | No | The paper specifies '10, 000 database images were used during training, and 4, 658 images for testing,' but does not explicitly mention a separate validation split or its size/proportion. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions deep learning models and architectures such as 'Res Net-101 [44]' and 'long short-term memory network (LSTM),' but it does not specify any software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | No | For details on architectural configurations, parameter settings, baseline implementation, please refer to Appendix D. |