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..
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives
Authors: Zhuofan Ying, Peter Hase, Mohit Bansal
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments on three benchmark datasets: CLEVR-XAI [5], GQA [21], and VQA-HAT [11]. |
| Researcher Affiliation | Academia | Department of Computer Science University of North Carolina at Chapel Hill EMAIL |
| Pseudocode | No | The paper describes methods using mathematical equations and prose, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | All supporting code for experiments in this paper is available at https://github.com/zfying/visfis. |
| Open Datasets | Yes | We perform experiments on three benchmark datasets: CLEVR-XAI [5], GQA [21], and VQA-HAT [11]. |
| Dataset Splits | Yes | Table 1: Dataset split sizes. Dataset Train Dev Test-ID Test-OOD CLEVR-XAI 83k 14k 21k 22k GQA-101k 101k 20k 20k 20k VQA-HAT 36k 6k 9k 9k |
| Hardware Specification | Yes | We use one NVIDIA A100 GPU for training. |
| Software Dependencies | No | The paper mentions using 'Py Torch' but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | All models are trained with Adam [29] optimizer with a learning rate of 1e-4, except for LXMERT which is 1e-5. We train each model for 20 epochs with a batch size of 64. |