Search Results for author: Olga Russakovsky

Found 37 papers, 20 papers with code

Gender Artifacts in Visual Datasets

no code implementations18 Jun 2022 Nicole Meister, Dora Zhao, Angelina Wang, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky

Gender biases are known to exist within large-scale visual datasets and can be reflected or even amplified in downstream models.

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

no code implementations15 Jun 2022 Vikram V. Ramaswamy, Sunnie S. Y. Kim, Nicole Meister, Ruth Fong, Olga Russakovsky

Specifically, we develop a novel explanation framework ELUDE (Explanation via Labelled and Unlabelled DEcomposition) that decomposes a model's prediction into two parts: one that is explainable through a linear combination of the semantic attributes, and another that is dependent on the set of uninterpretable features.

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

no code implementations6 Jun 2022 Zhiwei Deng, Olga Russakovsky

We propose an algorithm that compresses the critical information of a large dataset into compact addressable memories.

Continual Learning

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

1 code implementation10 May 2022 Angelina Wang, Vikram V. Ramaswamy, Olga Russakovsky

In this work, we grapple with questions that arise along three stages of the machine learning pipeline when incorporating intersectionality as multiple demographic attributes: (1) which demographic attributes to include as dataset labels, (2) how to handle the progressively smaller size of subgroups during model training, and (3) how to move beyond existing evaluation metrics when benchmarking model fairness for more subgroups.

Fairness

CARETS: A Consistency And Robustness Evaluative Test Suite for VQA

1 code implementation ACL 2022 Carlos E. Jimenez, Olga Russakovsky, Karthik Narasimhan

We introduce CARETS, a systematic test suite to measure consistency and robustness of modern VQA models through a series of six fine-grained capability tests.

Question Generation Visual Question Answering +1

Multi-query Video Retrieval

1 code implementation10 Jan 2022 Zeyu Wang, Yu Wu, Karthik Narasimhan, Olga Russakovsky

In this paper, we focus on the less-studied setting of multi-query video retrieval, where multiple queries are provided to the model for searching over the video archive.

Video Retrieval

HIVE: Evaluating the Human Interpretability of Visual Explanations

1 code implementation6 Dec 2021 Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky

Despite the recent growth of interpretability work, there is a lack of systematic evaluation of proposed techniques.

Scaling Fair Learning to Hundreds of Intersectional Groups

no code implementations29 Sep 2021 Eric Zhao, De-An Huang, Hao liu, Zhiding Yu, Anqi Liu, Olga Russakovsky, Anima Anandkumar

In real-world applications, however, there are multiple protected attributes yielding a large number of intersectional protected groups.

Fairness Knowledge Distillation

Understanding and Evaluating Racial Biases in Image Captioning

1 code implementation ICCV 2021 Dora Zhao, Angelina Wang, Olga Russakovsky

Image captioning is an important task for benchmarking visual reasoning and for enabling accessibility for people with vision impairments.

Image Captioning Visual Reasoning

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

1 code implementation RC 2020 Sunnie S. Y. Kim, Sharon Zhang, Nicole Meister, Olga Russakovsky

The implementation of most (7 of 10) methods was straightforward, especially after we received additional details from the original authors.

A Study of Face Obfuscation in ImageNet

1 code implementation10 Mar 2021 Kaiyu Yang, Jacqueline Yau, Li Fei-Fei, Jia Deng, Olga Russakovsky

In this paper, we explore the effects of face obfuscation on the popular ImageNet challenge visual recognition benchmark.

object-detection Object Detection +3

Directional Bias Amplification

1 code implementation24 Feb 2021 Angelina Wang, Olga Russakovsky

We introduce and analyze a new, decoupled metric for measuring bias amplification, $\text{BiasAmp}_{\rightarrow}$ (Directional Bias Amplification).

Fairness

A Technical and Normative Investigation of Social Bias Amplification

no code implementations1 Jan 2021 Angelina Wang, Olga Russakovsky

The conversation around the fairness of machine learning models is growing and evolving.

Fairness

Fair Attribute Classification through Latent Space De-biasing

1 code implementation CVPR 2021 Vikram V. Ramaswamy, Sunnie S. Y. Kim, Olga Russakovsky

Fairness in visual recognition is becoming a prominent and critical topic of discussion as recognition systems are deployed at scale in the real world.

Classification Fairness +1

Point and Ask: Incorporating Pointing into Visual Question Answering

1 code implementation27 Nov 2020 Arjun Mani, Nobline Yoo, Will Hinthorn, Olga Russakovsky

Concretely, we (1) introduce and motivate point-input questions as an extension of VQA, (2) define three novel classes of questions within this space, and (3) for each class, introduce both a benchmark dataset and a series of baseline models to handle its unique challenges.

Question Answering Visual Question Answering +1

Towards Unique and Informative Captioning of Images

1 code implementation ECCV 2020 Zeyu Wang, Berthy Feng, Karthik Narasimhan, Olga Russakovsky

We find that modern captioning systems return higher likelihoods for incorrect distractor sentences compared to ground truth captions, and that evaluation metrics like SPICE can be 'topped' using simple captioning systems relying on object detectors.

Image Captioning Re-Ranking

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

no code implementations31 Mar 2020 Felix Yu, Zhiwei Deng, Karthik Narasimhan, Olga Russakovsky

In the Vision-and-Language Navigation (VLN) task, an agent with egocentric vision navigates to a destination given natural language instructions.

Vision and Language Navigation

Compositional Temporal Visual Grounding of Natural Language Event Descriptions

no code implementations4 Dec 2019 Jonathan C. Stroud, Ryan McCaffrey, Rada Mihalcea, Jia Deng, Olga Russakovsky

Temporal grounding entails establishing a correspondence between natural language event descriptions and their visual depictions.

Visual Grounding

Human uncertainty makes classification more robust

no code implementations ICCV 2019 Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, Olga Russakovsky

We then show that, while contemporary classifiers fail to exhibit human-like uncertainty on their own, explicit training on our dataset closes this gap, supports improved generalization to increasingly out-of-training-distribution test datasets, and confers robustness to adversarial attacks.

Classification General Classification

CornerNet-Lite: Efficient Keypoint Based Object Detection

6 code implementations18 Apr 2019 Hei Law, Yun Teng, Olga Russakovsky, Jia Deng

Together these two variants address the two critical use cases in efficient object detection: improving efficiency without sacrificing accuracy, and improving accuracy at real-time efficiency.

object-detection Real-Time Object Detection

What Actions are Needed for Understanding Human Actions in Videos?

1 code implementation ICCV 2017 Gunnar A. Sigurdsson, Olga Russakovsky, Abhinav Gupta

We present the many kinds of information that will be needed to achieve substantial gains in activity understanding: objects, verbs, intent, and sequential reasoning.

Learning to Learn from Noisy Web Videos

no code implementations CVPR 2017 Serena Yeung, Vignesh Ramanathan, Olga Russakovsky, Liyue Shen, Greg Mori, Li Fei-Fei

Our method uses Q-learning to learn a data labeling policy on a small labeled training dataset, and then uses this to automatically label noisy web data for new visual concepts.

Action Recognition Q-Learning

Predictive-Corrective Networks for Action Detection

no code implementations CVPR 2017 Achal Dave, Olga Russakovsky, Deva Ramanan

While deep feature learning has revolutionized techniques for static-image understanding, the same does not quite hold for video processing.

Action Detection Optical Flow Estimation +1

Crowdsourcing in Computer Vision

no code implementations7 Nov 2016 Adriana Kovashka, Olga Russakovsky, Li Fei-Fei, Kristen Grauman

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts.

Object Recognition

Much Ado About Time: Exhaustive Annotation of Temporal Data

no code implementations25 Jul 2016 Gunnar A. Sigurdsson, Olga Russakovsky, Ali Farhadi, Ivan Laptev, Abhinav Gupta

We conclude that the optimal strategy is to ask as many questions as possible in a HIT (up to 52 binary questions after watching a 30-second video clip in our experiments).

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

no code implementations CVPR 2016 Serena Yeung, Olga Russakovsky, Greg Mori, Li Fei-Fei

In this work we introduce a fully end-to-end approach for action detection in videos that learns to directly predict the temporal bounds of actions.

Ranked #9 on Temporal Action Localization on THUMOS’14 (mAP IOU@0.2 metric)

Action Detection

What's the Point: Semantic Segmentation with Point Supervision

1 code implementation6 Jun 2015 Amy Bearman, Olga Russakovsky, Vittorio Ferrari, Li Fei-Fei

The semantic image segmentation task presents a trade-off between test time accuracy and training-time annotation cost.

Semantic Segmentation

Best of Both Worlds: Human-Machine Collaboration for Object Annotation

no code implementations CVPR 2015 Olga Russakovsky, Li-Jia Li, Li Fei-Fei

This paper brings together the latest advancements in object detection and in crowd engineering into a principled framework for accurately and efficiently localizing objects in images.

object-detection Object Detection

Joint calibration of Ensemble of Exemplar SVMs

no code implementations CVPR 2015 Davide Modolo, Alexander Vezhnevets, Olga Russakovsky, Vittorio Ferrari

We formulate joint calibration as a constrained optimization problem and devise an efficient optimization algorithm to find its global optimum.

object-detection Object Detection

ImageNet Large Scale Visual Recognition Challenge

9 code implementations1 Sep 2014 Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, Li Fei-Fei

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images.

General Classification Image Classification +3

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