Continuous Surface Embeddings
Authors: Natalia Neverova, David Novotny, Marc Szafraniec, Vasil Khalidov, Patrick Labatut, Andrea Vedaldi
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that the proposed approach performs on par or better than the state-of-the-art methods for dense pose estimation for humans, while being conceptually simpler. We also collect a new in-the-wild dataset of dense correspondences for animal classes and demonstrate that our framework scales naturally to the new deformable object categories. 5 Experiments. The experimental results are reported following the updated protocol based on GPSm scores [54]. |
| Researcher Affiliation | Industry | Natalia Neverova David Novotny Vasil Khalidov Marc Szafraniec Patrick Labatut Andrea Vedaldi {nneverova, dnovotny, vkhalidov, mszafraniec, plabatut, vedaldi} fb.com. Facebook AI Research |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The code, trained models and the dataset will be made publicly available to ensure reproducibility. |
| Open Datasets | Yes | We rely on the Dense Pose-COCO dataset [17] for evaluation of the proposed method on the human category and comparison with the Dense Pose (IUV) training. Additionally, we collect correspondence annotations on a set of 9 animal categories of the LVIS dataset [21]. Based on images from the COCO dataset [31], LVIS features significantly more accurate object masks. |
| Dataset Splits | Yes | The training is performed on 8 GPUs for 130k iterations on Dense Pose-COCO (standard s1x schedule [54]) and 5k iterations on Dense Pose-Chimps and Dense Pose-LVIS. Table 2: Performance on Dense Pose-COCO, with IUV (top) and CSE (bottom) training (GPSm scores, minival). Table 3: Hyperparameter search and performance in low data regimes (AP, Dense Pose COCO, minival). |
| Hardware Specification | No | The training is performed on 8 GPUs. This is a general statement and does not specify the model or type of GPUs, or any other hardware components. |
| Software Dependencies | No | Our networks are implemented in PyTorch within the Detectron2 [54] framework. The paper mentions software names but does not provide specific version numbers for reproducibility. |
| Experiment Setup | Yes | The training is performed on 8 GPUs for 130k iterations on Dense Pose-COCO (standard s1x schedule [54]) and 5k iterations on Dense Pose-Chimps and Dense Pose-LVIS. All CSE models are trained with loss Lσ (eq. 4), LBO size M = 256, embedding size D = 16. |