562 citations · 621 across the 15 of their papers we have counts for
15 papers
Obstructions to Spin(7) Nahm transforms on tori
Spencer Whitehead
The Nahm transform for 4-dimensional flat hyperkahler tori is an isometry between the moduli space of anti-self-dual (ASD) instantons on a torus and the moduli space of ASD i…
Improving Selective Visual Question Answering by Learning from Your Peers
Corentin Dancette, Spencer Whitehead, Rishabh Maheshwary +5
Despite advances in Visual Question Answering (VQA), the ability of models to assess their own correctness remains underexplored. Recent work has shown that VQA models, out-of-the-…
Simple Token-Level Confidence Improves Caption Correctness
Suzanne Petryk, Spencer Whitehead, Joseph E. Gonzalez +3
The ability to judge whether a caption correctly describes an image is a critical part of vision-language understanding. However, state-of-the-art models often misinterpret the cor…
Segment Anything
Alexander Kirillov, Eric Mintun, Nikhila Ravi +9
We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest seg…
Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly
Spencer Whitehead, Suzanne Petryk, Vedaad Shakib +4
Machine learning has advanced dramatically, narrowing the accuracy gap to humans in multimodal tasks like visual question answering (VQA). However, while humans can say "I don't kn…
Separating Skills and Concepts for Novel Visual Question Answering
Spencer Whitehead, Hui Wu, Heng Ji +2
Generalization to out-of-distribution data has been a problem for Visual Question Answering (VQA) models. To measure generalization to novel questions, we propose to separate them…
Learning from Lexical Perturbations for Consistent Visual Question Answering
Spencer Whitehead, Hui Wu, Yi Ren Fung +3
Existing Visual Question Answering (VQA) models are often fragile and sensitive to input variations. In this paper, we propose a novel approach to address this issue based on modul…
Global Attention for Name Tagging
Boliang Zhang, Spencer Whitehead, Lifu Huang +1
Many name tagging approaches use local contextual information with much success, but fail when the local context is ambiguous or limited. We present a new framework to improve name…
Cross-media Structured Common Space for Multimedia Event Extraction
Manling Li, Alireza Zareian, Qi Zeng +4
We introduce a new task, MultiMedia Event Extraction (M2E2), which aims to extract events and their arguments from multimedia documents. We develop the first benchmark and collect…
Classifying Regular Polyhedra and Polytopes using Wythoff's Construction
Spencer Whitehead
Wythoff's construction associates a uniform polytope to a Coxeter diagram whose vertices are decorated with crosses, which indicate the subgroup stabilizing a generic point. Champa…
Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks
Pavan Kapanipathi, Veronika Thost, Siva Sankalp Patel +10
Textual entailment is a fundamental task in natural language processing. Most approaches for solving the problem use only the textual content present in training data. A few approa…
A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving
Maxwell Crouse, Ibrahim Abdelaziz, Bassem Makni +7
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search. Deep reinforcement learning has been proposed as a way to o…
On a smoothness characterization for good moduli spaces
Dan Edidin, Matthew Satriano, Spencer Whitehead
Let be a smooth Artin stack with properly stable good moduli space . The purpose of this paper is to prove that a simple geometric criterion…
Paper Abstract Writing through Editing Mechanism
Qingyun Wang, Zhihao Zhou, Lifu Huang +4
We present a paper abstract writing system based on an attentive neural sequence-to-sequence model that can take a title as input and automatically generate an abstract. We design…
Entity-aware Image Caption Generation
Di Lu, Spencer Whitehead, Lifu Huang +2
Current image captioning approaches generate descriptions which lack specific information, such as named entities that are involved in the images. In this paper we propose a new ta…