Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Diversifying Spatial-Temporal Perception for Video Domain Generalization
Authors: Kun-Yu Lin, Jia-Run Du, Yipeng Gao, Jiaming Zhou, Wei-Shi Zheng
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 22,883 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,787 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 three benchmarks of different types demonstrate the effectiveness and versatility of our approach. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Sun Yat-sen University, China 2Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is released at https://github.com/Kunyu Lin/STDN/. |
| Open Datasets | Yes | UCF-HMDB is the most widely used benchmark for cross-domain video classification [15, 18], which contains 3,809 videos of 12 overlapping sport categories shared by UCF101 [94] and HMDB51 [95]. EPIC-Kitchens-DG is a cross-scene egocentric action recognition benchmark, which consists of 10,094 videos across 8 egocentric action classes from three domains (scenes), following Munro et al. [49]. Jester-DG is a cross-domain hand gesture recognition benchmark. We select videos from the Jester dataset [97] and construct two domains following Pan et al. [19]. |
| Dataset Splits | Yes | We split the source video set into training and validation sets following previous source validation protocols [25, 15], i.e., a reasonable in-domain model selection scheme for better generalization ability in unseen target domains. |
| Hardware Specification | Yes | All experiments are conducted by Py Torch [99] with four NVIDIA GTX 1080Ti GPUs. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | We take N = 5 frames for each video for temporal modeling. We set K = 4, τ = 0.5, Ds = 192 and Dt = 256. F( ) is a linear classifier and Frel( ) is an MLP classifier. All parameters are optimized using mini-batch SGD with a batch size of 32, a momentum of 0.9, a learning rate of 1e-3 and a weight decay of 5e-4. By default, the trade-off hyperparameters are set as λent = 0.1 and λrel = 0.5. |