The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering
Open accessAbstract
We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets. We have developed a strong and robust question engine that leverages Visual Genome scene graph structures to create 22M diverse reasoning questions, which all come with functional programs that represent their semantics. We use the programs to gain tight control over the answer distribution and present a new tunable smoothing technique to mitigate question biases. Accompanying the dataset is a suite of new metrics that evaluate essential qualities such as consistency, grounding and plausibility. A careful analysis is performed for baselines as well as state-of-the-art models, providing fine-grained results for different question types and topologies. Whereas a blind LSTM obtains a mere 42.1%, and strong VQA models achieve 54.1%, human performance tops at 89.3%, offering ample opportunity for new research to explore. We hope GQA will provide an enabling resource for the next generation of models with enhanced robustness, improved consistency, and deeper semantic understanding of vision and language.
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Cites
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“dataset, following Xu et al. [39] 4We expand the original Visual Genome dataset with 5k new scene graphs collected through crowdsourcing. 5Note that we cannot effectively use the wordnet annotations [26] used throughout the Visual Genome dataset since they are highly inaccurate, relating objects to irrelevant senses,…”
MethodVisual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
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Cited by
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Self-citationImproved Baselines with Visual Instruction Tuning
Liu, H., Li, C., Li, Y., & Lee, Y. J. (2023) · arXiv · doi:10.48550/arxiv.2310.03744 · Cited by 1,733
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
Li, X., Yin, X., Li, C., Zhang, P., Hu, X., Zhang, L., Wang, L., Hu, H., Dong, L., Wei, F., et al. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58577-8_8 · Cited by 1,542
Improved Baselines with Visual Instruction Tuning
Liu, H., Li, C., Li, Y., & Lee, Y. J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02484 · Cited by 1,748
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
Su, W., Zhu, X., Cao, Y., Li, B., Lu, L., Wei, F., & Dai, J. (2019) · arXiv · doi:10.48550/arxiv.1908.08530 · Cited by 950
Metadata
- Authors
- Hudson, D. A., & Manning, C. D.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 6693-6702
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2950104027
- Cited by
- 1,451
- References
- 45
- FWCI
- 38.54
- Type
- conference paper
- Language
- en
- Open access
- green
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