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..
SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
Authors: Hao Lu, Wenze Liu, Zixuan Ye, Hongtao Fu, Yuliang Liu, Zhiguo Cao
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate SAPA across a number of mainstream dense prediction tasks, for example: i) semantic segmentation: we test SAPA on several transformer-based segmentation baselines on the ADE20K dataset [6], such as Seg Former [7], Mask Former [8], and Mask2Former [9], improving the baselines by 1% 2.7% m Io U; ii) object detection: SAPA improves the performance of Faster R-CNN by 0.4% AP on MS COCO [10]; iii) monocular depth estimation: SAPA reduces the rmse metric of BTS [11] from 0.419 to 0.408 on NYU Depth V2 [12]; and iv) image matting: SAPA outperforms a strong A2U matting baseline [5] on the Adobe Composition-1k testing set [13] with a further 3.8% relative error reduction in the SAD metric. |
| Researcher Affiliation | Academia | Hao Lu Wenze Liu Zixuan Ye Hongtao Fu Yuliang Liu Zhiguo Cao School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074, China EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/poppinace/sapa |
| Open Datasets | Yes | For semantic segmentation, we conduct experiments on the ADE20K dataset [6]... For object detection, we use the MS COCO [10] dataset... For depth estimation, we use NYU Depth V2 dataset [12]... For image matting, we train the model on the Adobe Image Matting dataset [13]... |
| Dataset Splits | Yes | For depth estimation, we use NYU Depth V2 dataset [12] and its default train/test split. |
| Hardware Specification | Yes | All our experiments are run on a server with 8 NVIDIA Ge Force RTX 3090 GPUs. |
| Software Dependencies | No | The paper mentions software like 'mmdetection' but does not specify version numbers for any key software components or libraries. |
| Experiment Setup | Yes | All training settings and implementation details are kept the same as the original papers. ... We set d = 32 and K = 5 in SAPA. ... For object detection... we follow the 1 (12 epochs) training configurations. |