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..
Manta: Enhancing Mamba for Few-Shot Action Recognition of Long Sub-Sequence
Authors: Wenbo Huang, Jinghui Zhang, Guang Li, Lei Zhang, Shuoyuan Wang, Fang Dong, Jiahui Jin, Takahiro Ogawa, Miki Haseyama
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Manta achieves new state-of-the-art performance on prominent benchmarks, including SSv2, Kinetics, UCF101, and HMDB51. Extensive empirical studies prove that Manta significantly improves FSAR of long subsequence from multiple perspectives. |
| Researcher Affiliation | Academia | 1Southeast University, Nanjing 211189, Jiangsu, China 2Hokkaido University, Sapporo 060-0808, Hokkaido, Japan 3Nanjing Normal University, Nanjing 210023, Jiangsu, China 4Southern University of Science and Technology, Shenzhen 518055, Guangdong, China EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology with architectural diagrams and mathematical formulations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://github.com/wenbohuang1002/Manta |
| Open Datasets | Yes | Widely used benchmark datasets such as temporal-related SSv2 (Goyal et al. 2017), spatial-related Kinetics (Carreira and Zisserman 2017), UCF101 (Soomro, Zamir, and Shah 2012), and HMDB51 (Kuehne et al. 2011) are selected for proving the effectiveness of Manta. |
| Dataset Splits | Yes | According to the most common data split (Zhu and Yang 2018; Cao et al. 2020; Zhang et al. 2020), all datasets are divided into Dtrain, Dval, and Dtest (Dtrain Dval Dtest = ). |
| Hardware Specification | Yes | Most experiments are completed on a server with two 32GB NVIDIA Tesla V100 PCIe GPUs. |
| Software Dependencies | No | The paper mentions using specific backbones (Res Net-50, Vi T-B, VMamba-B) and an SGD optimizer, but it does not specify software dependencies with version numbers such as Python, PyTorch, or CUDA versions. |
| Experiment Setup | Yes | We adopt two standard few-shot settings including 5-way 1-shot and 5-shot to conduct experiments. ... Features extracted are 2048-dimensional vectors (D = 2048). ... Except for the larger SSv2 which requires 75,000 tasks training, other datasets utilize 10,000 tasks. An SGD optimizer with an initial learning rate of 10 3 is applied for training. The Dval determines hyper-parameters including multi-scale (O = {1, 2, 4}), temperature (τ = 0.07) and weight factor of loss (λ = 4). |