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..
Predicting Event Memorability from Contextual Visual Semantics
Authors: Qianli Xu, Fen Fang, Ana Molino, Vigneshwaran Subbaraju, Joo-Hwee Lim
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this study, we investigate factors that affect event memorability according to a cued recall process. Specifically, we explore whether event memorability is contingent on the event context, as well as the intrinsic visual attributes of image cues. We design a novel experiment protocol and conduct a large-scale experiment with 47 elder subjects over 3 months. Subjects memory of life events is tested in a cued recall process. Using advanced visual analytics methods, we build a first-of-its-kind event memorability dataset (called R3) with rich information about event context and visual semantic features. Furthermore, we propose a contextual event memory network (CEMNet) that tackles multi-modal input to predict item-wise event memorability, which outperforms competitive benchmarks. |
| Researcher Affiliation | Collaboration | Qianli Xu Institute for Infocomm Research A*STAR, Singapore EMAIL Fen Fang Institute for Infocomm Research A*STAR, Singapore EMAIL Ana Garcia del Molino Byte Dance AI Lab Singapore EMAIL Vigneshwaran Subbaraju Institute of High Performance Computing A*STAR, Singapore EMAIL Joo Hwee Lim Institute for Infocomm Research, A*STAR Nanyang Technological University, Singapore EMAIL |
| Pseudocode | No | The paper provides a model architecture diagram (Figure 2) and describes the components, but it does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Source code is available at https: //github.com/ffzzy840304/Predicting-Event-Memorability. |
| Open Datasets | No | The paper mentions creating and using the 'R3' dataset for its experiments, stating it is the 'first, large-scale dataset that includes rich visual and contextual information associated with event memory from the real-world context'. However, it does not provide any specific link, DOI, repository, or explicit statement of public availability for this dataset. |
| Dataset Splits | Yes | First, we randomly split the dataset with a 4:1 train-test ratio based on subject index. ... We perform 5 random splits and evaluate the average performance (i.e., 5-fold cross-validation). |
| Hardware Specification | Yes | Our models are trained (with Adam optimizer) and evaluated on Pytorch platform using a machine with NVIDIA Ge Force GTX 1080 GPU. |
| Software Dependencies | No | The paper mentions 'Pytorch platform' but does not specify its version or any other software dependencies with their specific version numbers (e.g., 'Python 3.x', 'CUDA x.x', or specific library versions). |
| Experiment Setup | Yes | For MLP, AMNet and ICNet, the batch size are set as 64, 16 and 32 respectively; learning rate are set as 0.001, 0.0001 and 0.0001; training epochs are set as 100. |