4 citations · 8 across the 6 of their papers we have counts for
10 papers
To Find Waldo You Need Contextual Cues: Debiasing Who's Waldo
Yiran Luo, Pratyay Banerjee, Tejas Gokhale +2
We present a debiased dataset for the Person-centric Visual Grounding (PCVG) task first proposed by Cui et al. (2021) in the Who's Waldo dataset. Given an image and a caption, PCVG…
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness
Tejas Gokhale, Swaroop Mishra, Man Luo +2
Data modification, either via additional training datasets, data augmentation, debiasing, and dataset filtering, has been proposed as an effective solution for generalizing to out-…
Improving Biomedical Information Retrieval with Neural Retrievers
Man Luo, Arindam Mitra, Tejas Gokhale +1
Information retrieval (IR) is essential in search engines and dialogue systems as well as natural language processing tasks such as open-domain question answering. IR serve an impo…
Weakly Supervised Relative Spatial Reasoning for Visual Question Answering
Pratyay Banerjee, Tejas Gokhale, Yezhou Yang +1
Vision-and-language (V\&L) reasoning necessitates perception of visual concepts such as objects and actions, understanding semantics and language grounding, and reasoning about the…
Self-Supervised Test-Time Learning for Reading Comprehension
Pratyay Banerjee, Tejas Gokhale, Chitta Baral
Recent work on unsupervised question answering has shown that models can be trained with procedurally generated question-answer pairs and can achieve performance competitive with s…
WeaQA: Weak Supervision via Captions for Visual Question Answering
Pratyay Banerjee, Tejas Gokhale, Yezhou Yang +1
Methodologies for training visual question answering (VQA) models assume the availability of datasets with human-annotated \textit{Image-Question-Answer} (I-Q-A) triplets. This has…