Extreme Low Resolution Activity Recognition With Multi-Siamese Embedding Learning
Authors: Michael Ryoo, Kiyoon Kim, Hyun Yang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally confirm that our approach of jointly learning such transform robust LR video representation and the classifier outperforms the previous state-of-the-art low resolution recognition approaches on two public standard datasets by a meaningful margin. |
| Researcher Affiliation | Collaboration | 1Ego Vid Inc., Daejeon, South Korea 2Indiana University, Bloomington, IN, USA 3Ulsan National Institute of Science and Technology, Ulsan, South Korea |
| Pseudocode | No | The paper provides mathematical equations to describe its loss functions and architecture components, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available or provide a link to a code repository. |
| Open Datasets | Yes | HMDB dataset (Kuehne et al. 2011) is one of the most widely used public video datasets...Dog Centric dataset (Iwashita et al. 2014) is a smaller scale dataset... |
| Dataset Splits | Yes | The standard evaluation setting of the dataset using 3 provided training/testing splits was followed... We followed the standard evaluation setting of the dataset, using 10 random half-training/half-testing splits... a standard early stopping strategy using validation errors was used to check the convergence, avoiding overfitting. |
| Hardware Specification | Yes | Our approach runs in real-time ( 50 fps) on a Nvidia Jetson TX2 mobile GPU card with the Tensor Flow library... |
| Software Dependencies | No | The paper mentions using the 'Tensor Flow library' and 'Farneback algorithm' but does not specify any version numbers for these or other software components like the 'TV-L1 optical flow extraction algorithm'. |
| Experiment Setup | No | The paper describes the model architecture, input dimensions (e.g., 16x12), the number of transforms used (n=75), and that a 'standard early stopping strategy' was employed. However, it does not provide specific hyperparameters such as learning rate, batch size, optimizer details, or the exact number of training epochs. |