BAR — A Reinforcement Learning Agent for Bounding-Box Automated Refinement
Authors: Morgane Ayle, Jimmy Tekli, Julia El-Zini, Boulos El-Asmar, Mariette Awad2561-2568
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results on a car industry-related dataset and on the PASCAL VOC dataset show a consistent increase of up to 0.28 in the Intersection-over-Union of bounding-boxes with their desired ground-truths, while saving 30%-82% of human intervention time in either correcting or re-drawing inaccurate proposals. |
| Researcher Affiliation | Collaboration | 1Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Lebanon 2Universite de Franche Comtee, Belfort, France 3BMW Group, Munich, Germany |
| Pseudocode | No | The paper describes algorithmic steps in prose but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | 2Code available at https://gitlab.com/Morgane Ayle/bar |
| Open Datasets | Yes | For a fair comparison with the literature, BAR-DRL is additionally evaluated on the aeroplane class of the publicly available PASCAL VOC dataset (Everingham et al. 2010). |
| Dataset Splits | Yes | There are approximately 800 available images for each class, 200 of which are used for testing. and BAR-DRL is trained on the latter [PASCAL 2007 trainval], with 309 training examples, β = 0.6 and c1 = 0.2. The pipeline is tested on PASCAL 2007 test data |
| Hardware Specification | Yes | The experiments were run on a Ge Force GTX 1080 with 11,000 Mi B memory and Cuda version 10.0. |
| Software Dependencies | Yes | The experiments were run on a Ge Force GTX 1080 with 11,000 Mi B memory and Cuda version 10.0. |
| Experiment Setup | Yes | The neural network consists of two fully-connected layers of 500 neurons each with Relu activation and random normal initialization, and an output layer of 9 neurons with linear activation. Mean square error loss with Adam optimizer and learning rate of 0.001 are used, and the discount factor γ for the Q-function is set to 0.90. BAR-DRL is trained for 80 epochs using an experience replay with maximum memory length of 2000, and epsilon-greedy exploration with ϵ = 1.0 initially and linearly annealing to 0.1 for 15 epochs. |