Exploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow
Authors: Hanyu Zhou, Yi Chang, Haoyue Liu, YAN WENDING, Yuxing Duan, Zhiwei Shi, Luxin Yan
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have been performed to verify the superiority of the proposed method.We conduct extensive experiments on synthetic and real datasets. The synthetic dataset is synthesized by the noise model (Zheng et al., 2020) on KITTI2015 (Menze & Geiger, 2015), named as (noise) Dark-KITTI2015. The real datasets include the public datasets (e.g., Dark-GOF and Dark-DSEC) and the proposed low light frame-event (LLFE) dataset. |
| Researcher Affiliation | Collaboration | 1National Key Lab of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology 2Huawei International Co. Ltd. {hyzhou,yichang,yanluxin}@hust.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | The synthetic dataset is synthesized by the noise model (Zheng et al., 2020) on KITTI2015 (Menze & Geiger, 2015), named as (noise) Dark-KITTI2015. The real datasets include the public datasets (e.g., Dark-GOF and Dark-DSEC) and the proposed low light frame-event (LLFE) dataset. Dark-GOF and Dark-DSEC are the nighttime parts of GOF (Li et al., 2021) and DSEC (Gehrig et al., 2021a). |
| Dataset Splits | No | The paper does not specify exact split percentages, absolute sample counts for each split, or references to predefined splits with citations for training, validation, or test sets. |
| Hardware Specification | No | The paper states: 'The computation is completed in the HPC Platform of Huazhong University of Science and Technology.' This is a general reference to a computing environment but does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions 'We set the sample number N as 1000 and the motion class number K as 10' and refers to 'weights that control the importance of the related losses' (λ1, ..., λ7), but does not provide concrete hyperparameter values such as learning rates, batch sizes, number of epochs, or specific optimizer settings for the experimental setup. |