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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Extreme Low Resolution Activity Recognition With Multi-Siamese Embedding Learning
Authors: Michael Ryoo, Kiyoon Kim, Hyun Yang
AAAI 2018 | Venue PDF | 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. |