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..
A Multimodal, Multi-Task Adapting Framework for Video Action Recognition
Authors: Mengmeng Wang, Jiazheng Xing, Boyuan Jiang, Jun Chen, Jianbiao Mei, Xingxing Zuo, Guang Dai, Jingdong Wang, Yong Liu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results validate the efficacy of our approach, demonstrating exceptional performance in supervised learning while maintaining strong generalization in zero-shot scenarios. |
| Researcher Affiliation | Collaboration | 1Zhejiang University 2Youtu Lab,Tencent 3Technical University of Munich 4SGIT AI Lab, State Grid Corporation of China 5Baidu Inc |
| Pseudocode | No | The paper describes the architecture and components using figures (e.g., Fig. 3a, 3b) and mathematical formulations, but it does not contain a dedicated pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We evaluate our M2-CLIP for supervised learning in two primary datasets: Kinetics-400 (K400) (Kay et al. 2017) and Something-Something-V2 (SSv2) (Goyal et al. 2017). For the generalization evaluation, we test our model on UCF101 (Soomro, Zamir, and Shah 2012) and HMDB51 (Kuehne et al. 2011). |
| Dataset Splits | No | The paper mentions using specific datasets (Kinetics-400, Something-Something-V2, UCF101, HMDB51) and a frame sampling strategy, but it does not explicitly provide details about train/validation/test dataset splits (e.g., percentages, sample counts, or a method for splitting). |
| Hardware Specification | No | The paper does not specify the hardware used to run the experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | We employ Vi T-B/16 based CLIP as our backbone and use a sparse frame sampling strategy with 8, 16, or 32 frames during training and inference. |