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Learning Informative Edge Maps for Indoor Scene Layout Prediction

• Arun Mallya, Svetlana Lazebnik
• 2015 IEEE International Conference on Computer…
• 2015

In this paper, we introduce new edge-based features for the task of recovering the 3D layout of an indoor scene from a single image. Indoor scenes have certain edges that are very informative about… (More)

Part Localization using Multi-Proposal Consensus for Fine-Grained Categorization

• Kevin J. Shih, Arun Mallya, Saurabh Singh, Derek Hoiem
• BMVC
• 2015

The most common approach to keypoint localization is to learn a set of keypoints detectors to model appearance and an associated spatial model [3, 4, 5, 9] to capture their spatial relations.… (More)

Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering

• Arun Mallya, Svetlana Lazebnik
• ECCV
• 2016

This paper proposes deep convolutional network models that utilize local and global context to make human activity label predictions in still images, achieving state-of-the-art performance on two… (More)

PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

• Arun Mallya, Svetlana Lazebnik
• 2018 IEEE/CVF Conference on Computer Vision and…
• 2018

This paper presents a method for adding multiple tasks to a single deep neural network while avoiding catastrophic forgetting. Inspired by network pruning techniques, we exploit redundancies in large… (More)

Phrase Localization and Visual Relationship Detection with Comprehensive Image-Language Cues

• Bryan A. Plummer, Arun Mallya, Christopher M. Cervantes, Julia Hockenmaier, Svetlana Lazebnik
• 2017 IEEE International Conference on Computer…
• 2017

This paper presents a framework for localization or grounding of phrases in images using a large collection of linguistic and visual cues. We model the appearance, size, and position of entity… (More)

Phrase Localization and Visual Relationship Detection with Comprehensive Linguistic Cues

• Bryan A. Plummer, Arun Mallya, Christopher M. Cervantes, Julia Hockenmaier, Svetlana Lazebnik
• ArXiv
• 2016

This paper presents a framework for localization or grounding of phrases in images using a large collection of linguistic and visual cues.1 We model the appearance, size, and position of entity… (More)

Local Learning for Mining Outlier Subgraphs from Network Datasets

• Manish Gupta, Arun Mallya, Subhro Roy, Jason H. D. Cho, Jiawei Han
• SDM
• 2014

In the real world, various systems can be modeled using entity-relationship graphs. Given such a graph, one may be interested in identifying suspicious or anomalous subgraphs. Specifically, a user… (More)

Piggyback: Adding Multiple Tasks to a Single, Fixed Network by Learning to Mask.

• Arun Mallya, Svetlana Lazebnik
• ArXiv
• 2018

This work presents a method for adding multiple tasks to a single, fixed deep neural network without affecting performance on already learned tasks. By building upon concepts from network… (More)

Solving Visual Madlibs with Multiple Cues

• Tatiana Tommasi, Arun Mallya, Bryan A. Plummer, Svetlana Lazebnik, Alexander C. Berg, Tamara L. Berg
• BMVC
• 2016

This paper focuses on answering multiple choice questions from the Visual Madlibs dataset [2] which was created by asking people to write fill-in-the-blank descriptions about persons (action,… (More)

Recurrent Models for Situation Recognition

• Arun Mallya, Svetlana Lazebnik
• 2017 IEEE International Conference on Computer…
• 2017

This work proposes Recurrent Neural Network (RNN) models to predict structured ‘image situations’ – actions and noun entities fulfilling semantic roles related to the action. In contrast to prior… (More)

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