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MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning.

, , , , , and . ICCV, page 13158-13168. IEEE, (2021)

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Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation., , , , , , and . LREC, page 1665-1670. European Language Resources Association, (2022)Where and Who? Automatic Semantic-Aware Person Composition., , , , and . WACV, page 1519-1528. IEEE Computer Society, (2018)High level describable attributes for predicting aesthetics and interestingness., , and . CVPR, page 1657-1664. IEEE Computer Society, (2011)Chat-crowd: A Dialog-based Platform for Visual Layout Composition., , , and . NAACL-HLT (Demonstrations), page 138-142. Association for Computational Linguistics, (2019)Improving Visual Grounding by Encouraging Consistent Gradient-Based Explanations., , , and . CVPR, page 19165-19174. IEEE, (2023)General Multi-Label Image Classification With Transformers., , , and . CVPR, page 16478-16488. Computer Vision Foundation / IEEE, (2021)PropTest: Automatic Property Testing for Improved Visual Programming., , , , and . CoRR, (2024)Learning from Models and Data for Visual Grounding., , , , and . CoRR, (2024)Going Beyond Nouns With Vision & Language Models Using Synthetic Data., , , , , , , , , and 1 other author(s). ICCV, page 20098-20108. IEEE, (2023)ReferItGame: Referring to Objects in Photographs of Natural Scenes., , , and . EMNLP, page 787-798. ACL, (2014)