Mark Yatskar

According to our database1, Mark Yatskar authored at least 40 papers between 2010 and 2024.

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Bibliography

2024
Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models.
CoRR, 2024

LLM-based Hierarchical Concept Decomposition for Interpretable Fine-Grained Image Classification.
CoRR, 2024

A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis.
CoRR, 2024

DOLOMITES: Domain-Specific Long-Form Methodical Tasks.
CoRR, 2024

What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perception.
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

ExpertQA: Expert-Curated Questions and Attributed Answers.
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

CoMo: Controllable Motion Generation Through Language Guided Pose Code Editing.
Proceedings of the Computer Vision - ECCV 2024, 2024

Holodeck: Language Guided Generation of 3D Embodied AI Environments.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
Pachinko: Patching Interpretable QA Models through Natural Language Feedback.
CoRR, 2023

Interpretable-by-Design Text Classification with Iteratively Generated Concept Bottleneck.
CoRR, 2023

Interpretable by Design Visual Question Answering.
CoRR, 2023

AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference.
Proceedings of the Findings of the Association for Computational Linguistics: EACL 2023, 2023

Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property Prediction.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and Models.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

2021
Induce, Edit, Retrieve: Language Grounded Multimodal Schema for Instructional Video Retrieval.
CoRR, 2021

Visual Goal-Step Inference using wikiHow.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

Visual Semantic Role Labeling for Video Understanding.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

Grounded Situation Recognition.
Proceedings of the Computer Vision - ECCV 2020, 2020

RoboTHOR: An Open Simulation-to-Real Embodied AI Platform.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

What Does BERT with Vision Look At?
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

2019
VisualBERT: A Simple and Performant Baseline for Vision and Language.
CoRR, 2019

Gender Bias in Contextualized Word Embeddings.
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019

A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC.
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019

Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases.
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

2018
Adversarial Removal of Gender from Deep Image Representations.
CoRR, 2018

Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods.
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018

QuAC: Question Answering in Context.
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

Neural Motifs: Scene Graph Parsing With Global Context.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints.
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017

Commonly Uncommon: Semantic Sparsity in Situation Recognition.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Neural AMR: Sequence-to-Sequence Models for Parsing and Generation.
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017

2016
Stating the Obvious: Extracting Visual Common Sense Knowledge.
Proceedings of the NAACL HLT 2016, 2016

Situation Recognition: Visual Semantic Role Labeling for Image Understanding.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

2014
See No Evil, Say No Evil: Description Generation from Densely Labeled Images.
Proceedings of the Third Joint Conference on Lexical and Computational Semantics, 2014

2013
Learning to Relate Literal and Sentimental Descriptions of Visual Properties.
Proceedings of the Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, 2013

2010
For the sake of simplicity: Unsupervised extraction of lexical simplifications from Wikipedia.
Proceedings of the Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, 2010


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