3 citations · 3 across the 5 of their papers we have counts for
5 papers
TABLET: A Large-Scale Dataset for Robust Visual Table Understanding
Iñigo Alonso, Imanol Miranda, Eneko Agirre +1
While table understanding increasingly relies on pixel-only settings, current benchmarks predominantly use synthetic renderings that lack the complexity and visual diversity of rea…
Adding simple structure at inference improves Vision-Language Compositionality
Imanol Miranda, Ander Salaberria, Eneko Agirre +1
Dual encoder Vision-Language Models (VLM) such as CLIP are widely used for image-text retrieval tasks. However, those models struggle with compositionality, showing a bag-of-words-…
BiVLC: Extending Vision-Language Compositionality Evaluation with Text-to-Image Retrieval
Imanol Miranda, Ander Salaberria, Eneko Agirre +1
Existing Vision-Language Compositionality (VLC) benchmarks like SugarCrepe are formulated as image-to-text retrieval problems, where, given an image, the models need to select betw…
Grounding Spatial Relations in Text-Only Language Models
Gorka Azkune, Ander Salaberria, Eneko Agirre
This paper shows that text-only Language Models (LM) can learn to ground spatial relations like "left of" or "below" if they are provided with explicit location information of obje…
Unsupervised Domain Adaption for Neural Information Retrieval
Carlos Dominguez, Jon Ander Campos, Eneko Agirre +1
Neural information retrieval requires costly annotated data for each target domain to be competitive. Synthetic annotation by query generation using Large Language Models or rule-b…