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..
Generalized Video Moment Retrieval
Authors: Qin You, Qilong Wu, Yicong Li, Wei Ji, Li Li, Pengcheng Cai, Lina Wei, Roger Zimmermann
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 EXPERIMENT 5.1 EVALUATION METRICS 5.2 IMPLEMENTATION DETAILS 5.3 COMPARISON WITH STATE-OF-THE-ART METHODS 5.4 ABLATION STUDY |
| Researcher Affiliation | Academia | 1Nanjing University, 2National University of Singapore, 3Shanghai AI Laboratory 4University of Southern California, 5Zhejiang University EMAIL, EMAIL EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods and equations but does not present any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code: https://github.com/42xingxing/NEx T-VMR |
| Open Datasets | Yes | We build a specialized dataset, NEx T-VMR, which is derived from the YFCC100M dataset (Thomee et al., 2016) after meticulous construction and analysis. This dataset is tailored specifically for GVMR, featuring a diverse array of query types, including one-to-multi and no-target queries. |
| Dataset Splits | No | Figure 4 shows the distribution of these queries in the train, validation and test sets. This distribution reflects the diversity and balance of the dataset. |
| Hardware Specification | Yes | All speed tests were conducted on a single NVIDIA RTX A40 GPU. |
| Software Dependencies | No | The paper mentions several pre-trained models and optimizers (e.g., Slow Fast, CLIP, Adam W) but does not provide specific version numbers for software libraries like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | During training, Adam W (Ilya Loshchilov, 2019) optimizer with weight decay 1e-4 is adopted; the batch size is set at 32 for training and 128 for testing; the hidden dimension C = 256. We configured our transformer encoder and decoder with two layers each, denoted as T = 2. The hyperparameter settings were determined as follows: L = 10, λc = 4, λl1 = 10, λiou = 1, λbcl = λproxy = 0.1, for optimal performance. For the no-target threshold we set it as δ = 0.7 which is experimentally balance for target and no-target generalization. |