Swarm Heuristics based Adaptive and Penalized Estimation of Splines
SHAPES is a dataset of synthetic images designed to benchmark systems for understanding of spatial and logical relations among multiple objects. The dataset consists of complex questions about arrangements of colored shapes. The questions are built around compositions of concepts and relations, e.g. Is there a red shape above a circle? or Is a red shape blue?. Questions contain between two and four attributes, object types, or relationships. There are 244 questions and 15,616 images in total, with all questions having a yes and no answer (and corresponding supporting image). This eliminates the risk of learning biases.
Each image is a 30×30 RGB image depicting a 3×3 grid of objects. Each object is characterized by shape (circle, square, triangle), colour (red, green, blue) and size (small, big).
Source: Visual Question Answering: A Survey of Methods and Datasets
Image Source: https://github.com/ronghanghu/n2nmn#train-and-evaluate-on-the-shapes-dataset
Variants: SHAPES
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| Time Series Classification | FCN-SNLST | Seq2Tens: An Efficient Representation of … | 2020-06-12 |
| Time Series Classification | SNLST | Seq2Tens: An Efficient Representation of … | 2020-06-12 |
| Time Series Classification | GP-KConv1D | Bayesian Learning from Sequential Data … | 2019-06-19 |
| Time Series Classification | GP-Sig-LSTM | Bayesian Learning from Sequential Data … | 2019-06-19 |
| Time Series Classification | GP-LSTM | Bayesian Learning from Sequential Data … | 2019-06-19 |
| Time Series Classification | GP-GRU | Bayesian Learning from Sequential Data … | 2019-06-19 |
| Time Series Classification | GP-Sig-GRU | Bayesian Learning from Sequential Data … | 2019-06-19 |
| Time Series Classification | GP-Sig | Bayesian Learning from Sequential Data … | 2019-06-19 |
| Time Series Classification | MALSTM-FCN | Multivariate LSTM-FCNs for Time Series … | 2018-01-14 |
Recent papers with results on this dataset: