Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing
Authors: Aadarsh Sahoo, Rutav Shah, Rameswar Panda, Kate Saenko, Abir Das
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several benchmark datasets demonstrate the superiority of our proposed approach over state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Aadarsh Sahoo1 Rutav Shah1 Rameswar Panda2 Kate Saenko2,3 Abir Das1 1 IIT Kharagpur, 2 MIT-IBM Watson AI Lab, 3 Boston University |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | Yes | Project page: https://cvir.github.io/projects/comix. |
| Open Datasets | Yes | We evaluate the performance of our approach using several publicly available benchmark datasets for video domain adaptation, namely UCF-HMDB [7], Jester [53], and Epic-Kitchens [50]. |
| Dataset Splits | Yes | We use the standard training and testing splits provided by the authors in [7, 53, 50] to conduct our experiments on each dataset. |
| Hardware Specification | Yes | We use 6 NVIDIA Tesla V100 GPUs for training all our models. |
| Software Dependencies | No | The paper mentions specific models and optimizers (e.g., I3D, SGD) but does not provide specific version numbers for software libraries or frameworks like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | We use an initial learning rate of 0.001 for the I3D and 0.01 for the GCN in all our experiments. We use a batch size of 40 equally split over the two domains... The temperature parameter is set to τ = 0.5. ... We use a pseudo-label threshold of 0.7 in all our experiments and smooth the cross-entropy loss with ϵ = 0.1... |