A Self Validation Network for Object-Level Human Attention Estimation
Authors: Zehua Zhang, Chen Yu, David Crandall
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate on two public datasets, demonstrating that the Self Validation Module significantly benefits both training and testing and that our model outperforms the state-of-the-art. |
| Researcher Affiliation | Academia | Zehua Zhang,1 Chen Yu,2 David Crandall1 1Luddy School of Informatics, Computing, and Engineering 2Department of Psychological and Brain Sciences Indiana University Bloomington {zehzhang, chenyu, djcran}@indiana.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | More information is available at http://vision.soic.indiana.edu/mindreader/. This link points to a project page, not an explicit code repository or a statement of code release for the methodology. |
| Open Datasets | Yes | We evaluate on two public datasets, ATT [68] (Adult-Toddler Toy play) and Epic-Kitchen Dataset [13]. |
| Dataset Splits | Yes | We randomly select 90% of the samples in each object class for training and use the remaining 10% for testing, resulting in about 17, 000 training and 1, 900 testing samples, each with 15 continuous frames. ... We randomly select 90% of samples for training, yielding about 120, 000 training and 13, 000 testing samples. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, or memory) used for running its experiments. |
| Software Dependencies | No | We implemented our model with Keras [12] and Tensorflow [1]. No specific version numbers are provided for these software dependencies. |
| Experiment Setup | Yes | We use stochastic gradient descent with learning rate 0.03, momentum 0.9, decay 0.0001, and L2 regularizer 5e 5. The loss function consists of four parts: global classification Lglobalclass, attention Lattn, anchor box classification Lboxclass, and box regression Lbox, Ltotal = αLglobalclass + βLattn + 1 Npos (γLboxclass + Lbox), where we empirically set α = β = γ = 1. |