Zero-Shot Semantic Segmentation
Authors: Maxime Bucher, Tuan-Hung VU, Matthieu Cord, Patrick Pérez
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report evaluations of ZS3Net on two datasets (Pascal-VOC and Pascal-Context) and in zero-shot setups with varying numbers of unseen classes. Compared to a ZSL baseline, our method delivers excellent performances, which are further boosted using self-training and semantic contextual cues. |
| Researcher Affiliation | Collaboration | Maxime Bucher valeo.ai maxime.bucher@valeo.com Tuan-Hung Vu valeo.ai tuan-hung.vu@valeo.com Matthieu Cord Sorbonne Université valeo.ai matthieu.cord@lip6.fr Patrick Pérez valeo.ai patrick.perez@valeo.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | Experimental evaluation is done on the two datasets: Pascal-VOC 2012 [15] and Pascal Context [31]. Pascal-VOC contains 1, 464 training images with segmentation annotations of 20 object classes. Similar to [11], we adopt additional supervision from semantic boundary annotations [19] during training. Pascal-Context provides dense semantic segmentation annotations for Pascal-VOC 2010, which comprises 4, 998 training and 5, 105 validation images of 59 object/stuff classes. |
| Dataset Splits | Yes | Pascal-Context provides dense semantic segmentation annotations for Pascal-VOC 2010, which comprises 4, 998 training and 5, 105 validation images of 59 object/stuff classes. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like Deep Labv3+, ResNet-101, SGD, Adam optimizer, and word2vec, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Segmentation models are trained by SGD [5] optimizer using polynomial learning rate decay with the base learning rate of 7e 3, weight decay 5e 4 and momentum 0.9. The GMMN is a multi-layer perceptron with one hidden layer, leaky-RELU non-linearity [29] and dropout [41]. In our experiments, we fix the number of hidden neurons to 256 and set the kernel bandwidths as {2, 5, 10, 20, 40, 60}. The input Gaussian noise has the same dimension as used w2c embeddings, namely 300. The generative model is trained using Adam optimizer [21] with the learning rate of 2e 4. |