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..
DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection
Authors: Jia S Lim, Zhuoxiao Chen, Zhi Chen, Mahsa Baktashmotlagh, Xin Yu, Zi Huang, Yadan Luo
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of Di PEx through extensive class-agnostic OD and OOD-OD experiments on MS-COCO and LVIS, surpassing other prompting methods by up to 20.1% in AR and achieving a 21.3% AP improvement over SAM. |
| Researcher Affiliation | Academia | The University of Queensland EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 The Proposed Di PEx for Class-Agnostic Object Detection |
| Open Source Code | Yes | The code is available at https://github.com/jason-lim26/Di PEx. |
| Open Datasets | Yes | Datasets. We conduct our experiments using two detection datasets: 1). MS-COCO [32], a large-scale object detection and instance segmentation dataset, comprising approximately 115K training images and 5K validation images across 80 classes. 2). LVIS [15] includes 2.2 million high-quality instance segmentation masks covering 1,000 class labels, resulting in a long-tailed data distribution. It consists of around 100K training images and 19.8K validation images. |
| Dataset Splits | Yes | Datasets. We conduct our experiments using two detection datasets: 1). MS-COCO [32], a large-scale object detection and instance segmentation dataset, comprising approximately 115K training images and 5K validation images across 80 classes. 2). LVIS [15]... It consists of around 100K training images and 19.8K validation images. |
| Hardware Specification | Yes | Our code is developed on the Open Grounding-DINO framework [63], and operates on a single NVIDIA RTX A6000 GPU with 48 GB of memory. |
| Software Dependencies | No | The paper states: “Our code is developed on the Open Grounding-DINO framework [63],” but does not provide specific version numbers for this framework or any other software dependencies like Python, PyTorch, etc. |
| Experiment Setup | Yes | For our experiments, we choose a batch size of 8 for training, and set hyperparameter γ = 0.1, τp = 0.1, τc = 0.1, θ = 15 , K = 9, L = 3, and while adopting all remaining hyperparameters from the Open Grounding-DINO codebase. |