Space-Time Correspondence as a Contrastive Random Walk
Authors: Allan Jabri, Andrew Owens, Alexei Efros
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the learned representation on video label propagation tasks involving objects, keypoints, and semantic parts, by using it as a similarity metric. We also study the effects of edge dropout, training sequence length, and self-supervised adaptation at test-time. In addition to comparison with the state-of-the-art, we consider a baseline of label propagation with strong pre-trained features. |
| Researcher Affiliation | Academia | Allan A. Jabri UC Berkeley Andrew Owens University of Michigan Alexei A. Efros UC Berkeley |
| Pseudocode | Yes | Algorithm 1 provides complete pseudocode for the method. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code for the described methodology or a direct link to a code repository. |
| Open Datasets | Yes | We train φ using the (unlabeled) videos from Kinetics400 [14], with Algorithm 1. We evaluate our model on DAVIS 2017 [83], a popular benchmark for video object segmentation... We consider pose tracking on the JHMDB benchmark... We consider the semantic part segmentation task of the Video Instance Parsing (VIP) benchmark [120] |
| Dataset Splits | No | The paper mentions using specific datasets (Kinetics400, DAVIS 2017, JHMDB, VIP) for training and evaluation, but it does not explicitly provide the train/test/validation split percentages or sample counts used for these datasets within the main text. |
| Hardware Specification | No | The paper mentions 'compute resources donated by NVIDIA' in the acknowledgments but does not provide specific details on the GPU models, CPU models, or other hardware specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions a 'PyTorch-like style' for its pseudocode and references libraries and optimizers like ResNet-18 [42] and Adam [49], but it does not provide specific version numbers for PyTorch or any other software dependencies required to replicate the experiments. |
| Experiment Setup | Yes | We train φ using the (unlabeled) videos from Kinetics400 [14], with Algorithm 1. We used the Adam optimizer [49] for two million updates with a learning rate of 1 10 4. We use a temperature of = 0.07 in Equation 1, following [113] and resize frames to 256 256 (before extracting nodes, as above). Except when indicated otherwise, we report results with edge dropout rate 0.1 and a videos of length 10. |