Search Results for author: Aditya Khosla

Found 18 papers, 7 papers with code

Following Gaze in Video

no code implementations ICCV 2017 Adria Recasens, Carl Vondrick, Aditya Khosla, Antonio Torralba

In this paper, we present an approach for following gaze in video by predicting where a person (in the video) is looking even when the object is in a different frame.

Network Dissection: Quantifying Interpretability of Deep Visual Representations

1 code implementation CVPR 2017 David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, Antonio Torralba

Given any CNN model, the proposed method draws on a broad data set of visual concepts to score the semantics of hidden units at each intermediate convolutional layer.


Following Gaze Across Views

no code implementations9 Dec 2016 Adrià Recasens, Carl Vondrick, Aditya Khosla, Antonio Torralba

In this paper, we present an approach for following gaze across views by predicting where a particular person is looking throughout a scene.

Places: An Image Database for Deep Scene Understanding

no code implementations6 Oct 2016 Bolei Zhou, Aditya Khosla, Agata Lapedriza, Antonio Torralba, Aude Oliva

The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification at tasks such as object and scene recognition.

BIG-bench Machine Learning Classification +4

Deep Learning for Identifying Metastatic Breast Cancer

3 code implementations18 Jun 2016 Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad, Andrew H. Beck

The International Symposium on Biomedical Imaging (ISBI) held a grand challenge to evaluate computational systems for the automated detection of metastatic breast cancer in whole slide images of sentinel lymph node biopsies.

General Classification Image Classification +1

Eye Tracking for Everyone

2 code implementations CVPR 2016 Kyle Krafka, Aditya Khosla, Petr Kellnhofer, Harini Kannan, Suchendra Bhandarkar, Wojciech Matusik, Antonio Torralba

We believe that we can put the power of eye tracking in everyone's palm by building eye tracking software that works on commodity hardware such as mobile phones and tablets, without the need for additional sensors or devices.

Gaze Estimation

Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition

no code implementations12 Jan 2016 Radoslaw M. Cichy, Aditya Khosla, Dimitrios Pantazis, Antonio Torralba, Aude Oliva

The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans.

Object Recognition

Learning Deep Features for Discriminative Localization

33 code implementations CVPR 2016 Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba

In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network to have remarkable localization ability despite being trained on image-level labels.

Weakly-Supervised Object Localization

Understanding and Predicting Image Memorability at a Large Scale

no code implementations ICCV 2015 Aditya Khosla, Akhil S. Raju, Antonio Torralba, Aude Oliva

Progress in estimating visual memorability has been limited by the small scale and lack of variety of benchmark data.

What Makes an Object Memorable?

no code implementations ICCV 2015 Rachit Dubey, Joshua Peterson, Aditya Khosla, Ming-Hsuan Yang, Bernard Ghanem

We augment both the images and object segmentations from the PASCAL-S dataset with ground truth memorability scores and shed light on the various factors and properties that make an object memorable (or forgettable) to humans.


Where are they looking?

no code implementations NeurIPS 2015 Adria Recasens, Aditya Khosla, Carl Vondrick, Antonio Torralba

Humans have the remarkable ability to follow the gaze of other people to identify what they are looking at.

Object Detectors Emerge in Deep Scene CNNs

1 code implementation22 Dec 2014 Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba

With the success of new computational architectures for visual processing, such as convolutional neural networks (CNN) and access to image databases with millions of labeled examples (e. g., ImageNet, Places), the state of the art in computer vision is advancing rapidly.

General Classification Object +3

ImageNet Large Scale Visual Recognition Challenge

12 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 +4

3D ShapeNets: A Deep Representation for Volumetric Shapes

no code implementations CVPR 2015 Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, Jianxiong Xiao

Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representations automatically.

Ranked #35 on 3D Point Cloud Classification on ModelNet40 (Mean Accuracy metric)

3D Point Cloud Classification 3D Shape Representation +2

Looking Beyond the Visible Scene

no code implementations CVPR 2014 Aditya Khosla, Byoungkwon An An, Joseph J. Lim, Antonio Torralba

In this work, we propose to look beyond the visible elements of a scene; we demonstrate that a scene is not just a collection of objects and their configuration or the labels assigned to its pixels - it is so much more.

Scene Understanding

Large-Scale Video Summarization Using Web-Image Priors

no code implementations CVPR 2013 Aditya Khosla, Raffay Hamid, Chih-Jen Lin, Neel Sundaresan

Given the enormous growth in user-generated videos, it is becoming increasingly important to be able to navigate them efficiently.

Navigate Video Summarization

Inverting and Visualizing Features for Object Detection

no code implementations11 Dec 2012 Carl Vondrick, Aditya Khosla, Tomasz Malisiewicz, Antonio Torralba

By visualizing feature spaces, we can gain a more intuitive understanding of our detection systems.

Object object-detection +1

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