Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization
Authors: Jiyoung Kim, Kyuhong Shim, Insu Lee, Byonghyo Shim
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments demonstrate that EQUSS achieves state-of-the-art results on three standard benchmarks. |
| Researcher Affiliation | Academia | Jiyoung Kim, Kyuhong Shim, Insu Lee, Byonghyo Shim Department of Electrical and Computer Engineering, Seoul National University, Korea {jykim, khshim, islee, bshim}@islab.snu.ac.kr |
| Pseudocode | No | The paper describes the model architecture and training objective using descriptive text and mathematical equations, but does not include any formal pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the described methodology (EQUSS) is publicly available. |
| Open Datasets | Yes | We evaluate EQUSS on three standard semantic segmentation datasets and compare with the recent SOTA methods. From our empirical experiments on Coco Stuff-27 (Caesar, Uijlings, and Ferrari 2018), Cityscapes (Cordts et al. 2016), and Potsdam-3 USS benchmarks, we show that the proposed EQUSS outperforms the recent SOTA (Hamilton et al. 2022) by a substantial margin. |
| Dataset Splits | No | The paper mentions training images and evaluation processes (linear probing, unsupervised clustering) but does not explicitly provide details about training, validation, and test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU models, CPU types, or cloud computing specifications. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions) that would be required to reproduce the experiments. |
| Experiment Setup | Yes | The overall objective function is the sum of the training loss for the expansion head Lhead (Hamilton et al. 2022), codebook loss Lcodebook, and commitment loss Lcommit: L = Lhead + λ1Lcodebook + λ2Lcommit where λ1, λ2 > 0 are weighting coefficients. To study the effect of the number of codebooks (M) and the size of the codebook (K), we conduct experiments while varying M and K with fixed feature dimension. |