SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
Authors: Hao Lu, Wenze Liu, Zixuan Ye, Hongtao Fu, Yuliang Liu, Zhiguo Cao
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 {hlu,zgcao}@hust.edu.cn |
| 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. |