Charts are very popular for analyzing data. When exploring charts, people often ask a variety of complex reasoning questions that involve several logical and arithmetic operations. They also commonly refer to visual features of a chart in their questions. However, most existing datasets do not focus on such complex reasoning questions as their questions are template-based and answers come from a fixed-vocabulary. In this work, we present a large-scale benchmark covering 9.6K human-written questions as well as 23.1K questions generated from human-written chart summaries. To address the unique challenges in our benchmark involving visual and logical reasoning over charts, we present two transformer-based models that combine visual features and the data table of the chart in a unified way to answer questions. While our models achieve the state-of-the-art results on the previous datasets as well as on our benchmark, the evaluation also reveals several challenges in answering complex reason
160 PAPERS • 1 BENCHMARK
FigureQA is a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images. The images are synthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts.
46 PAPERS • 1 BENCHMARK
DVQA is a synthetic question-answering dataset on images of bar-charts.
41 PAPERS • 1 BENCHMARK
PlotQA is a VQA dataset with 28.9 million question-answer pairs grounded over 224,377 plots on data from real-world sources and questions based on crowd-sourced question templates. Existing synthetic datasets (FigureQA, DVQA) for reasoning over plots do not contain variability in data labels, real-valued data, or complex reasoning questions. Consequently, proposed models for these datasets do not fully address the challenge of reasoning over plots. In particular, they assume that the answer comes either from a small fixed size vocabulary or from a bounding box within the image. However, in practice this is an unrealistic assumption because many questions require reasoning and thus have real valued answers which appear neither in a small fixed size vocabulary nor in the image. In this work, we aim to bridge this gap between existing datasets and real world plots by introducing PlotQA. Further, 80.76% of the out-of-vocabulary (OOV) questions in PlotQA have answers that are not in a fixed
38 PAPERS • 5 BENCHMARKS
The MMVP (Multimodal Visual Patterns) Benchmark focuses on identifying "CLIP-blind pairs" – images that appear similar to the CLIP model despite having clear visual differences. These patterns highlight the challenges these systems face in answering straightforward questions, often leading to incorrect responses and hallucinated explanations.
30 PAPERS • NO BENCHMARKS YET
LEAF-QA, a comprehensive dataset of 250,000 densely annotated figures/charts, constructed from real-world open data sources, along with ~2 million question-answer (QA) pairs querying the structure and semantics of these charts. LEAF-QA highlights the problem of multimodal QA, which is notably different from conventional visual QA (VQA), and has recently gained interest in the community. Furthermore, LEAF-QA is significantly more complex than previous attempts at chart QA, viz. FigureQA and DVQA, which present only limited variations in chart data. LEAF-QA being constructed from real-world sources, requires a novel architecture to enable question answering.
6 PAPERS • NO BENCHMARKS YET
RealCQA Scientific Chart Question Answering as a Test-bed for First-Order Logic
5 PAPERS • 1 BENCHMARK
We present a new collection of 1,981 Vega-Lite specifications, which is used to demonstrate the generalizability and viability of our NL generation framework. This collection is the largest set of human-generated charts obtained from GitHub to date. It covers varying levels of complexity from a simple line chart without any interaction to a chart with four plots where data points are linked with selection interactions. Compared to the benchmarks, our dataset shows the highest average pairwise edit distance between specifications, which proves that the charts are highly diverse from one another. Moreover, it contains the largest number of charts with composite views, interactions (e.g., tooltips, panning & zooming, and linking), and diverse chart types (e.g., map, grid & matrix, diagram, etc.).
1 PAPER • NO BENCHMARKS YET