Olga Russakovsky

research

∙ 08/15/2023

Multimodal Dataset Distillation for Image-Text Retrieval

Dataset distillation methods offer the promise of reducing a large-scale...

0 Xindi Wu, et al. ∙

research

∙ 06/07/2023

ICON^2: Reliably Benchmarking Predictive Inequity in Object Detection

As computer vision systems are being increasingly deployed at scale in h...

0 Sruthi Sudhakar, et al. ∙

research

∙ 06/07/2023

Art and the science of generative AI: A deeper dive

A new class of tools, colloquially called generative AI, can produce hig...

0 Ziv Epstein, et al. ∙

research

∙ 05/15/2023

Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application

Trust is an important factor in people's interactions with AI systems. H...

0 Sunnie S. Y. Kim, et al. ∙

research

∙ 03/27/2023

UFO: A unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations for CNNs

Concept-based explanations for convolutional neural networks (CNNs) aim ...

0 Vikram V. Ramaswamy, et al. ∙

research

∙ 03/10/2023

Overcoming Bias in Pretrained Models by Manipulating the Finetuning Dataset

Transfer learning is beneficial by allowing the expressive features of m...

0 Angelina Wang, et al. ∙

research

∙ 02/16/2023

Boundary Guided Mixing Trajectory for Semantic Control with Diffusion Models

Applying powerful generative denoising diffusion models (DDMs) for downs...

5 Ye Zhu, et al. ∙

research

∙ 01/05/2023

Beyond web-scraping: Crowd-sourcing a geographically diverse image dataset

Current dataset collection methods typically scrape large amounts of dat...

8 Vikram V. Ramaswamy, et al. ∙

research

∙ 10/02/2022

"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction

Despite the proliferation of explainable AI (XAI) methods, little is und...

11 Sunnie S. Y. Kim, et al. ∙

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...

0 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...

43 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 ...

0 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 ...

0 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...

27 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...

0 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...

0 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 ...

2 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...

0 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...

6 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 ...

19 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...

12 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...

8 Kaiyu Yang, et al. ∙

research

∙ 02/24/2021

Directional Bias Amplification

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

0 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...

3 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 ...

0 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 ...

7 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...

11 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...

0 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...

0 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...

9 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...

6 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...

0 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...

0 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...

1 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...

20 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 ...

0 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...

0 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. ...

0 Siddha Ganju, et al. ∙

research

∙ 04/12/2017

Predictive-Corrective Networks for Action Detection

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

0 Achal Dave, et al. ∙

research

∙ 11/07/2016

Crowdsourcing in Computer Vision

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

0 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 ...

0 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...

0 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...

0 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...

0 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....

0 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 ...

0 Olga Russakovsky, et al. ∙

Olga Russakovsky

Featured Co-authors