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..
Learnability Matters: Active Learning for Video Captioning
Authors: Yiqian Zhang, Buyu Liu, Jun Bao, Qiang Huang, Min Zhang, Jun Yu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results on publicly available video captioning datasets with diverse video captioning models demonstrate that our algorithm outperforms SOTA active learning methods by a large margin,e.g.we achieve about 103% of full performance on CIDEr with 25% of human annotations on MSR-VTT. |
| Researcher Affiliation | Collaboration | 1Hangzhou Dianzi University 2Harbin Institute of Technology (Shenzhen) 3National University of Singapore EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Our full caption-wise algorithm is summarized in Alg. 1. |
| Open Source Code | Yes | Our code and model will be made available. |
| Open Datasets | Yes | We conduct our experiments on two datasets, MSVD Chen and Dolan (2011a) and MSRVTT Xu et al. (2016). |
| Dataset Splits | Yes | For each dataset, we follow their standard splits and report our active learning performance on their test sets. To mimic the learning process, we initialize L with 5% of data randomly selected from the training set, including both videos and their annotations. |
| Hardware Specification | Yes | All experiments are conducted on 4 RTX 3090 GPUs and 4 RTX 4090 GPUs with Pytorch Paszke et al. (2019), Huggingface transformers Wolf et al. (2020). |
| Software Dependencies | Yes | All experiments are conducted on 4 RTX 3090 GPUs and 4 RTX 4090 GPUs with Pytorch Paszke et al. (2019), Huggingface transformers Wolf et al. (2020). |
| Experiment Setup | Yes | The hyper-parameters λ1, λ2, λ3 in Eq. 5 are 3, 1, and 2, respectively. And they are chosen based on experiments on the validation set via grid search. Our re-ranking factor q in Eq. 6 is set to 10 on MSR-VTT. Meanwhile, R is set to 3, dividing the ranked videos into 3 regions according to ˆLn. Specifically, both the first and last regions consist of 2000 videos. At,r equals to 2, 1, and 0 with r = {1, 2, 3}. |