Discovering Informative and Robust Positives for Video Domain Adaptation
Authors: Chang Liu, Kunpeng Li, Michael Stopa, Jun Amano, Yun Fu
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTWe conducted experiments to assess our method on two prevalent benchmark datasets for video domain adaptation, specifically UCF HMDB (Chen et al., 2019a) and Epic-Kitchens (Munro & Damen, 2020). |
| Researcher Affiliation | Collaboration | 1Northeastern University, Boston, MA, USA 2Konica Minolta, San Mateo, CA, USA |
| Pseudocode | Yes | Algorithm 1 Robust Cross-domain Positives for video DA |
| Open Source Code | No | The paper states 'all related publications and source codes are cited appropriately' but does not explicitly state that *their* code is being released or provide a link to it. |
| Open Datasets | Yes | UCF HMDB is first assembled by Chen et al. (Chen et9al., 2019a) for studying video domain adaptation problem. This dataset is a subset of the UCF (Soomro et al., 2012) and HMDB datasets (Kuehne et al., 2011)... Epic-Kitchens... from the full Epic-Kitchens dataset (Damen et al., 2018). |
| Dataset Splits | No | To ensure consistency with prior research, we utilized the training and testing partitions provided by the respective authors in (Chen et al., 2019a; Munro & Damen, 2020). The paper explicitly mentions training and testing partitions, but does not specify validation splits. |
| Hardware Specification | Yes | We use four 12G NVIDIA GPUs for training. |
| Software Dependencies | No | The paper mentions using I3D and SGD but does not provide specific version numbers for software dependencies like PyTorch, Python, or CUDA. |
| Experiment Setup | Yes | We follow the standard pre-train then adapt procedure... train the model... for 3 epochs as a warm start... train models... for 40 epochs in total. For the hyperparameters, we select λ from {5, 10, 15, 20}, α0 from {0.1, 0.25, 0.5, 0.75} and the number of neighbors k from {1, 2, 5, 10, 20}. We set k = 5, λ = 15 and α0 = 0.25 for all datasets. We train all the models end-to-end using SGD with a momentum of 0.9 and a weight decay of 1e-7. We use an initial learning rate of 0.01 for the I3D with a cosine learning rate scheduler for our experiments. We use a batch size of 32 equally split over the two domains. |