Olga Russakovsky

research

∙ 07/27/2022

SiRi: A Simple Selective Retraining Mechanism for Transformer-based Visual Grounding

In this paper, we investigate how to achieve better visual grounding wit...

Mengxue Qu, et al. ∙

research

∙ 07/20/2022

Overlooked factors in concept-based explanations: Dataset choice, concept salience, and human capability

Concept-based interpretability methods aim to explain deep neural networ...

Vikram V. Ramaswamy, et al. ∙

research

∙ 07/07/2022

Predicting Word Learning in Children from the Performance of Computer Vision Systems

For human children as well as machine learning systems, a key challenge ...

Sunayana Rane, et al. ∙

research

∙ 06/18/2022

Gender Artifacts in Visual Datasets

Gender biases are known to exist within large-scale visual datasets and ...

Nicole Meister, et al. ∙

research

∙ 06/15/2022

ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features

Deep learning models have achieved remarkable success in different areas...

Vikram V. Ramaswamy, et al. ∙

research

∙ 06/06/2022

Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks

We propose an algorithm that compresses the critical information of a la...

Zhiwei Deng, et al. ∙

research

∙ 05/10/2022

Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation

Research in machine learning fairness has historically considered a sing...

Angelina Wang, et al. ∙

research

∙ 03/15/2022

CARETS: A Consistency And Robustness Evaluative Test Suite for VQA

We introduce CARETS, a systematic test suite to measure consistency and ...

Carlos E. Jimenez, et al. ∙

research

∙ 01/10/2022

Multi-query Video Retrieval

Retrieving target videos based on text descriptions is a task of great p...

Zeyu Wang, et al. ∙

research

∙ 12/06/2021

HIVE: Evaluating the Human Interpretability of Visual Explanations

As machine learning is increasingly applied to high-impact, high-risk do...

Sunnie S. Y. Kim, et al. ∙

research

∙ 06/16/2021

Understanding and Evaluating Racial Biases in Image Captioning

Image captioning is an important task for benchmarking visual reasoning ...

Dora Zhao, et al. ∙

research

∙ 04/28/2021

[Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias

Singh et al. (2020) point out the dangers of contextual bias in visual r...

Sunnie S. Y. Kim, et al. ∙

research

∙ 03/10/2021

A Study of Face Obfuscation in ImageNet

Face obfuscation (blurring, mosaicing, etc.) has been shown to be effect...

Kaiyu Yang, et al. ∙

research

∙ 02/24/2021

Directional Bias Amplification

Mitigating bias in machine learning systems requires refining our unders...

Angelina Wang, et al. ∙

research

∙ 12/02/2020

Fair Attribute Classification through Latent Space De-biasing

Fairness in visual recognition is becoming a prominent and critical topi...

Vikram V. Ramaswamy, et al. ∙

research

∙ 11/27/2020

Point and Ask: Incorporating Pointing into Visual Question Answering

Visual Question Answering (VQA) has become one of the key benchmarks of ...

Arjun Mani, et al. ∙

research

∙ 09/08/2020

Towards Unique and Informative Captioning of Images

Despite considerable progress, state of the art image captioning models ...

Zeyu Wang, et al. ∙

research

∙ 07/11/2020

Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation

The ability to perform effective planning is crucial for building an ins...

Zhiwei Deng, et al. ∙

research

∙ 04/16/2020

ViBE: A Tool for Measuring and Mitigating Bias in Image Datasets

Machine learning models are known to perpetuate the biases present in th...

Angelina Wang, et al. ∙

research

∙ 03/31/2020

Take the Scenic Route: Improving Generalization in Vision-and-Language Navigation

In the Vision-and-Language Navigation (VLN) task, an agent with egocentr...

Felix Yu, et al. ∙

research

∙ 12/16/2019

Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy

Computer vision technology is being used by many but remains representat...

Kaiyu Yang, et al. ∙

research

∙ 12/04/2019

Compositional Temporal Visual Grounding of Natural Language Event Descriptions

Temporal grounding entails establishing a correspondence between natural...

Jonathan C. Stroud, et al. ∙

research

∙ 11/26/2019

Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation

Computer vision models learn to perform a task by capturing relevant sta...

Zeyu Wang, et al. ∙

research

∙ 08/19/2019

Human uncertainty makes classification more robust

The classification performance of deep neural networks has begun to asym...

Joshua C. Peterson, et al. ∙

research

∙ 08/07/2019

SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition

Understanding the spatial relations between objects in images is a surpr...

Kaiyu Yang, et al. ∙

research

∙ 04/18/2019

CornerNet-Lite: Efficient Keypoint Based Object Detection

Keypoint-based methods are a relatively new paradigm in object detection...

Hei Law, et al. ∙

research

∙ 08/09/2017

What Actions are Needed for Understanding Human Actions in Videos?

What is the right way to reason about human activities? What directions ...

Gunnar A. Sigurdsson, et al. ∙

research

∙ 06/09/2017

Learning to Learn from Noisy Web Videos

Understanding the simultaneously very diverse and intricately fine-grain...

Serena Yeung, et al. ∙

research

∙ 04/12/2017

What's in a Question: Using Visual Questions as a Form of Supervision

Collecting fully annotated image datasets is challenging and expensive. ...

Siddha Ganju, et al. ∙

research

∙ 04/12/2017

Predictive-Corrective Networks for Action Detection

While deep feature learning has revolutionized techniques for static-ima...

Achal Dave, et al. ∙

research

∙ 11/07/2016

Crowdsourcing in Computer Vision

Computer vision systems require large amounts of manually annotated data...

Adriana Kovashka, et al. ∙

research

∙ 07/25/2016

Much Ado About Time: Exhaustive Annotation of Temporal Data

Large-scale annotated datasets allow AI systems to learn from and build ...

Gunnar A. Sigurdsson, et al. ∙

research

∙ 11/22/2015

End-to-end Learning of Action Detection from Frame Glimpses in Videos

In this work we introduce a fully end-to-end approach for action detecti...

Serena Yeung, et al. ∙

research

∙ 07/21/2015

Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos

Every moment counts in action recognition. A comprehensive understanding...

Serena Yeung, et al. ∙

research

∙ 06/06/2015

What's the Point: Semantic Segmentation with Point Supervision

The semantic image segmentation task presents a trade-off between test t...

Amy Bearman, et al. ∙

research

∙ 03/02/2015

Joint calibration of Ensemble of Exemplar SVMs

We present a method for calibrating the Ensemble of Exemplar SVMs model....

Davide Modolo, et al. ∙

research

∙ 09/01/2014

ImageNet Large Scale Visual Recognition Challenge

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in ...

Olga Russakovsky, et al. ∙

Olga Russakovsky

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