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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
ZeroMamba: Exploring Visual State Space Model for Zero-Shot Learning
Authors: Wenjin Hou, Dingjie Fu, Kun Li, Shiming Chen, Hehe Fan, Yi Yang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on four prominent ZSL benchmarks, Zero Mamba demonstrates superior performance, significantly outperforming the state-of-the-art (i.e., CNN-based and Vi T-based) methods under both conventional ZSL (CZSL) and generalized ZSL (GZSL) settings. |
| Researcher Affiliation | Academia | 1Re LER Lab, Zhejiang University, China 2Huazhong University of Science and Technology (HUST), China 3Mohamed bin Zayed University of AI EMAIL |
| Pseudocode | No | The paper describes the proposed method using mathematical formulations (Eq 1-14) and block diagrams (Fig 2) but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://github.com/Houwenjin/Zero Mamba |
| Open Datasets | Yes | To evaluate the effectiveness of our proposed framework, we conduct extensive experiments on three prominent ZSL datasets: Caltech-USCD Birds-200-2011 (CUB) (Welinder et al. 2010), SUN Attribute (SUN) (Patterson and Hays 2012) and Animals with Attributes 2 (AWA2) (Xian et al. 2019). We use the Proposed Split (PS) (Xian et al. 2019) division, as detailed in Tab. 3. Additionally, we verify the generalization of Zero Mamba on large-scale Image Net (Russakovsky et al. 2015) benchmark. |
| Dataset Splits | Yes | We use the Proposed Split (PS) (Xian et al. 2019) division, as detailed in Tab. 3. [...] We randomly split the training/test set into 800/200. We only use 1/10 of the data for every class for training. |
| Hardware Specification | Yes | We implement our experiments in Py Torch and utilize the SGD optimizer (momentum = 0.9, weight decay = 0.001) with learning rate of 5 10 4 on a single NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions "Py Torch" but does not specify a version number, preventing reproducible software dependency. |
| Experiment Setup | Yes | We implement our experiments in Py Torch and utilize the SGD optimizer (momentum = 0.9, weight decay = 0.001) with learning rate of 5 10 4 on a single NVIDIA A100 GPU. All models are trained with a batch size of 16. In our method, we empirically set {λsc, λcol} to {1.0,0.3}, {0.2,0.35}, and {0.0,0.98} for CUB, SUN, and AWA2, respectively. |