Visual Similarity Attention
Authors: Meng Zheng, Srikrishna Karanam, Terrence Chen, Richard J. Radke, Ziyan Wu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on three different tasks: image retrieval (Sec. 4.1), person re-identification (Sec. 4.2), and one-shot semantic segmentation (Sec. 4.3) to demonstrate the efficacy and generality of our proposed framework. We use a pretrained ResNet50 as our base architecture and implement all our code in Pytorch. |
| Researcher Affiliation | Collaboration | 1United Imaging Intelligence, Cambridge MA, USA 2Rensselaer Polytechnic Institute, Troy NY, USA |
| Pseudocode | No | The paper describes the proposed method in text and uses figures but does not include any formal 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 for the methodology described. |
| Open Datasets | Yes | We conduct experiments on the CUB200 (CUB) [Wah et al., 2011], Cars-196 (Cars) [Krause et al., 2013] and Stanford Online Products (SOP) [Song et al., 2016] datasets... We evaluated on the CUHK03-NP dataset (CUHK) [Zhong et al., 2017] and Duke MTMC-reid (Duke) [Ristani et al., 2016] datasets... We use the PASCAL 5i dataset (Pascal) [Shaban et al., 2017] for all experiments... |
| Dataset Splits | No | The paper mentions using 'training data' and 'testing data' but does not explicitly specify distinct train/validation/test splits, their percentages, or sample counts needed for reproduction. It mentions following protocols from cited works, but doesn't detail the splits within the paper itself. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU specifications, or memory. |
| Software Dependencies | No | The paper states 'implement all our code in Pytorch' but does not provide a specific version number for PyTorch or any other software dependencies, which is necessary for reproducibility. |
| Experiment Setup | Yes | We set γ = 0.2 and train the model for 40 epochs with the Adam optimizer. |