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 | Archive PDF | Plain Text | 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. |