Search Results for author:
Found 17 papers, 11 papers with code
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.
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.
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 #28 on
Question Answering
on TriviaQA
(using extra training data)
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 #3 on
Only Connect Walls Dataset Task 1 (Grouping)
on OCW
(Wasserstein Distance (WD) metric, using extra
training data)
Citation Intent Classification
Conversational Response Selection
+8
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.
Ranked #26 on
Question Answering
on BoolQ
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 (VQA)
on VQA-CP
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).
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.
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.
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
Visual Question Answering (VQA)
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.
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, to natural language processing tasks such as question answering and paraphrasing.
Ranked #1 on
Object Segmentation
on GRIT
I Can't Believe There's No Images! Learning Visual Tasks Using only Language Supervision
We produce models using only text training data on four representative tasks: image captioning, visual entailment, visual question answering and visual news captioning, and evaluate them on standard benchmarks using images.
Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models
As general purpose vision models get increasingly effective at a wide set of tasks, it is imperative that they be consistent across the tasks they support.
Holodeck: Language Guided Generation of 3D Embodied AI Environments
3D simulated environments play a critical role in Embodied AI, but their creation requires expertise and extensive manual effort, restricting their diversity and scope.
Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action
We present Unified-IO 2, the first autoregressive multimodal model that is capable of understanding and generating image, text, audio, and action.
