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..
Zero-Shot Semantic Segmentation
Authors: Maxime Bucher, Tuan-Hung VU, Matthieu Cord, Patrick Pérez
NeurIPS 2019 | Venue PDF | 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 EMAIL Tuan-Hung Vu valeo.ai EMAIL Matthieu Cord Sorbonne Université valeo.ai EMAIL Patrick Pérez valeo.ai EMAIL |
| 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. |