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Found 3 papers, 1 papers with code
Labeling Indoor Scenes with Fusion of Out-of-the-Box Perception Models
We aim to develop a cost-effective labeling approach to obtain pseudo-labels for semantic segmentation and object instance detection in indoor environments, with the ultimate goal of facilitating the training of lightweight models for various downstream tasks.
Towards Grounded Visual Spatial Reasoning in Multi-Modal Vision Language Models
In this work, we show qualitatively (using explainability tools) and quantitatively (using object detectors) that the poor object localization "grounding" ability of the models is a contributing factor to the poor image-text matching performance.
Towards More Equitable Question Answering Systems: How Much More Data Do You Need?
Question answering (QA) in English has been widely explored, but multilingual datasets are relatively new, with several methods attempting to bridge the gap between high- and low-resourced languages using data augmentation through translation and cross-lingual transfer.
