Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Hybrid Mamba for Few-Shot Segmentation
Authors: Qianxiong Xu, Xuanyi Liu, Lanyun Zhu, Guosheng Lin, Cheng Long, Ziyue Li, Rui Zhao
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 30,477 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,319 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have been conducted on two public benchmarks, showing the superiority of HMNet. ... 5 Experiments |
| Researcher Affiliation | Collaboration | Qianxiong Xu1, Xuanyi Liu2, Lanyun Zhu3, Guosheng Lin1 , Cheng Long1 , Ziyue Li4, Rui Zhao5 1S-Lab, Nanyang Technological University 2Peking University 3Singapore University of Technology and Design 4University of Cologne 5Sense Time Research |
| Pseudocode | No | The paper describes its methods using equations and diagrams (e.g., Figure 2, 3, 6) but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/Sam1224/HMNet. |
| Open Datasets | Yes | The methods are evaluated on two benchmark datasets, including PASCAL-5i [34] and COCO-20i [28]. |
| Dataset Splits | Yes | Both of them are evenly split into four folds based on the classes, and each fold would consist of 5 and 20 classes for PASCAL-5i and COCO-20i, respectively. Then, cross validations are carried out, with each fold being taken as the test set once, while the union of other folds is adopted for training. |
| Hardware Specification | Yes | We enable DDP for model training, e.g., use 4 and 8 NVIDIA V100 GPUs for two datasets. |
| Software Dependencies | No | The paper mentions using 'Adam W' and 'SGD' optimizers but does not specify version numbers for any software dependencies like programming languages or libraries. |
| Experiment Setup | Yes | We use Adam W to optimize Mamba-related parameters [5], and SGD to optimize the remaining parameters (e.g., decoder), with their learning rates initialized as 6e-5 and 5e-3. ... the model is trained for 300 epochs on PASCAL-5i, and 75 epochs on COCO-20i, with batch size set as 8 and 16, respectively. ... all images are randomly cropped to 473 473 and 633 633 for PASCAL-5i and COCO-20i, ... We employ 8 Mamba blocks (i.e., 4 self and hybrid Mamba pairs), and set the hidden dimension as 256. |