Search Results for author: Christopher Clark

Found 17 papers, 11 papers with code

Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models

1 code implementation28 Mar 2023 Adyasha Maharana, Amita Kamath, Christopher Clark, Mohit Bansal, Aniruddha Kembhavi

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.

I Can't Believe There's No Images! Learning Visual Tasks Using only Language Supervision

1 code implementation ICCV 2023 Sophia Gu, Christopher Clark, Aniruddha Kembhavi

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.

Image Captioning Question Answering +2

Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks

no code implementations17 Jun 2022 Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, Aniruddha Kembhavi

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.

Depth Estimation Image Generation +12

A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge

1 code implementation3 Jun 2022 Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, Roozbeh Mottaghi

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.

Question Answering Visual Question Answering +1

Webly Supervised Concept Expansion for General Purpose Vision Models

no code implementations4 Feb 2022 Amita Kamath, Christopher Clark, Tanmay Gupta, Eric Kolve, Derek Hoiem, Aniruddha Kembhavi

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.

Human-Object Interaction Detection Image Retrieval +4

Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles

1 code implementation Findings of the Association for Computational Linguistics 2020 Christopher Clark, Mark Yatskar, Luke Zettlemoyer

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.

Natural Language Inference Question Answering +2

Long-distance Detection of Bioacoustic Events with Per-channel Energy Normalization

no code implementations1 Nov 2019 Vincent Lostanlen, Kaitlin Palmer, Elly Knight, Christopher Clark, Holger Klinck, Andrew Farnsworth, Tina Wong, Jason Cramer, Juan Pablo Bello

This paper proposes to perform unsupervised detection of bioacoustic events by pooling the magnitudes of spectrogram frames after per-channel energy normalization (PCEN).

Noise Estimation speech-recognition +1

Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases

3 code implementations IJCNLP 2019 Christopher Clark, Mark Yatskar, Luke Zettlemoyer

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.

Natural Language Inference Question Answering +1

Deep contextualized word representations

46 code implementations NAACL 2018 Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer

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

Simple and Effective Multi-Paragraph Reading Comprehension

1 code implementation ACL 2018 Christopher Clark, Matt Gardner

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)

Question Answering Reading Comprehension +1

Teaching Deep Convolutional Neural Networks to Play Go

1 code implementation10 Dec 2014 Christopher Clark, Amos Storkey

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.

Game of Go

Classification for Big Dataset of Bioacoustic Signals Based on Human Scoring System and Artificial Neural Network

no code implementations15 May 2013 Mohammad Pourhomayoun, Peter Dugan, Marian Popescu, Denise Risch, Hal Lewis, Christopher Clark

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.

Classification General Classification +1

Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network

no code implementations15 May 2013 Mohammad Pourhomayoun, Peter Dugan, Marian Popescu, Christopher Clark

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.

General Classification

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