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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing
Authors: Aadarsh Sahoo, Rutav Shah, Rameswar Panda, Kate Saenko, Abir Das
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 23,507 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,317 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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... |