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..
Depth Privileged Object Detection in Indoor Scenes via Deformation Hallucination
Authors: Zhijie Zhang, Yan Liu, Junjie Chen, Li Niu, Liqing Zhang3456-3464
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on NYUDv2 and SUN RGB-D demonstrate the effectiveness of our method against the state-of-the-art methods for depth privileged object detection. |
| Researcher Affiliation | Academia | Mo E Key Lab of Artificial Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methods in text and diagrams, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing its source code or a link to a code repository. |
| Open Datasets | Yes | NYU Depth V2 (NYUDv2) (Silberman et al. 2012) consists of 1449 paired RGB-D images. SUN RGB-D (Song, Lichtenberg, and Xiao 2015) is composed of an official train/test split with 5285 and 5050 images, respectively. |
| Dataset Splits | Yes | The dataset [NYUDv2] is split into training (795 images) and test (654 images) sets. SUN RGB-D (Song, Lichtenberg, and Xiao 2015) is composed of an official train/test split with 5285 and 5050 images, respectively. |
| Hardware Specification | Yes | All experiments are conducted on Ubuntu 18.04 with two 8GB Ge Force RTX 2080 SUPER, 16GB Intel 9700K, and Py Torch 1.2.0 on Python 3.7. |
| Software Dependencies | Yes | All experiments are conducted on Ubuntu 18.04 with two 8GB Ge Force RTX 2080 SUPER, 16GB Intel 9700K, and Py Torch 1.2.0 on Python 3.7. |
| Experiment Setup | Yes | We train our model using the SGD optimizer for 50k iterations for D-branch pre-training and the whole model training. The basic learning rate is initialized to 1 10 3 and reduced to 1 10 4 when the iterations reach 40k. The weight decay and momentum are set to 5 10 4 and 0.9, respectively. The random seed is set to 222. two trade-off parameters α and β are set as 1.0 and 2.0, respectively. δ is a hyper-parameter controlling the intensity of avoiding negative transfer and set as 0.25 via cross-validation. µ is a trade-off parameter and set as 0.1 via corss-validation. |