Search Results for author:
Found 13 papers, 7 papers with code
Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks
We propose Unified-IO, a model that performs a large variety of AI tasks spanning classical computer vision tasks, including pose estimation, object detection, depth estimation and image generation, vision-and-language tasks such as region captioning and referring expression comprehension, to natural language processing tasks such as question answering and paraphrasing.
Ranked #1 on
Object Segmentation
on GRIT
A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge
In contrast to the existing knowledge-based VQA datasets, the questions generally cannot be answered by simply querying a knowledge base, and instead require some form of commonsense reasoning about the scene depicted in the image.
Webly Supervised Concept Expansion for General Purpose Vision Models
This work presents an effective and inexpensive alternative: learn skills from supervised datasets, learn concepts from web image search, and leverage a key characteristic of GPVs: the ability to transfer visual knowledge across skills.
Ranked #2 on
Object Localization
on GRIT
Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text
We investigate these challenges in the context of Iconary, a collaborative game of drawing and guessing based on Pictionary, that poses a novel challenge for the research community.
Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles
We evaluate performance on synthetic datasets, and four datasets built to penalize models that exploit known biases on textual entailment, visual question answering, and image recognition tasks.
Long-distance Detection of Bioacoustic Events with Per-channel Energy Normalization
This paper proposes to perform unsupervised detection of bioacoustic events by pooling the magnitudes of spectrogram frames after per-channel energy normalization (PCEN).
Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases
Our method has two stages: we (1) train a naive model that makes predictions exclusively based on dataset biases, and (2) train a robust model as part of an ensemble with the naive one in order to encourage it to focus on other patterns in the data that are more likely to generalize.
Ranked #5 on
Visual Question Answering
on VQA-CP
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
In this paper we study yes/no questions that are naturally occurring --- meaning that they are generated in unprompted and unconstrained settings.
Deep contextualized word representations
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).
Ranked #2 on
Citation Intent Classification
on ACL-ARC
(using extra training data)
Citation Intent Classification
Conversational Response Selection
+7
Simple and Effective Multi-Paragraph Reading Comprehension
We consider the problem of adapting neural paragraph-level question answering models to the case where entire documents are given as input.
Ranked #12 on
Question Answering
on TriviaQA
Teaching Deep Convolutional Neural Networks to Play Go
Our final networks are able to achieve move prediction accuracies of 41. 1% and 44. 4% on two different Go datasets, surpassing previous state of the art on this task by significant margins.
Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network
In this paper, we develop a novel method based on machine-learning and image processing to identify North Atlantic right whale (NARW) up-calls in the presence of high levels of ambient and interfering noise.
Classification for Big Dataset of Bioacoustic Signals Based on Human Scoring System and Artificial Neural Network
In this paper, we propose a method to improve sound classification performance by combining signal features, derived from the time-frequency spectrogram, with human perception.
