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..
Glance and Focus: Memory Prompting for Multi-Event Video Question Answering
Authors: Ziyi Bai, Ruiping Wang, Xilin Chen
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on four Multi-Event Video QA benchmarks including STAR, Ego Task QA, AGQA, and NEx T-QA. Our proposed model achieves state-of-the-art results, surpassing current large models in various challenging reasoning tasks. |
| Researcher Affiliation | Academia | 1Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China 2University of Chinese Academy of Sciences, Beijing, 100049, China |
| Pseudocode | No | The paper does not contain a pseudocode block or a clearly labeled algorithm block. |
| Open Source Code | Yes | The code and models are available at https://github.com/ByZ0e/Glance-Focus. |
| Open Datasets | Yes | We conduct extensive experiments on four Multi-Event Video QA benchmarks including STAR, Ego Task QA, AGQA, and NEx T-QA. |
| Dataset Splits | Yes | For each benchmark, we follow standard protocols outlined by prior works for data processing, metrics, and settings to ensure fair comparisons. |
| Hardware Specification | Yes | All experiments are conducted on an NVIDIA Ge Force RTX 3090Ti GPU. |
| Software Dependencies | No | The paper mentions software components like S3D, C3D, Faster-RCNN, CLIP, Transformer, RoBERTa, and Adam optimizer, but does not provide specific version numbers for these or other ancillary software dependencies. |
| Experiment Setup | Yes | We employ a standard 2-layer, 8-head Transformer Encoder-Decoder with hidden size D of 512 as the backbone for our Glance-Focus model. ... For training details, we use dropout of 0.1, and initialize model weights using Xavier init[13]. Adam optimizer[20] is used with a learning rate of 5e-6 to optimize model parameters. |