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..
Deconfounded Visual Grounding
Authors: Jianqiang Huang, Yu Qin, Jiaxin Qi, Qianru Sun, Hanwang Zhang998-1006
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On popular benchmarks, RED improves various state-of-the-art grounding methods by a significant margin. |
| Researcher Affiliation | Collaboration | 1Nanyang Technological University, Singapore 2Damo Academy, Alibaba Group 3Singapore Management University |
| Pseudocode | Yes | Algorithm 1: Visual Grounding with RED |
| Open Source Code | Yes | Code is available at: https://github.com/JianqiangH/DeconfoundedVG. |
| Open Datasets | Yes | Ref COCO, Ref COCO+ and Ref COCOg are three visual grounding benchmarks and their images are from MS-COCO (Lin et al. 2014). |
| Dataset Splits | Yes | Ref COCO (Yu et al. 2016) has ... is split into train/ validation/ test A/ test B with 120,624/ 10,834/ 5,657/ 5,095 images, respectively. |
| Hardware Specification | Yes | Under fair settings, we test the speed of Yang s-V1 and Yang s-V1+RED on a single Tesla V100. |
| Software Dependencies | No | The paper mentions using BERT and K-Means but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We deployed the K-Means algorithm to cluster those into N = 10 clusters forming the confounder dictionary Dg in Eq. (7)." and "After N exceeding 10, the performance won t show further improvement, thus we set N = 10. |