Search Results for author:
Found 18 papers, 7 papers with code
Following Gaze in Video
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
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
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
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.
Eye Tracking for Everyone
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.
Deep Learning for Identifying Metastatic Breast Cancer
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.
Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition
The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans.
Learning Deep Features for Discriminative Localization
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.
What Makes an Object Memorable?
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.
Understanding and Predicting Image Memorability at a Large Scale
Progress in estimating visual memorability has been limited by the small scale and lack of variety of benchmark data.
Where are they looking?
Humans have the remarkable ability to follow the gaze of other people to identify what they are looking at.
Visualizing Object Detection Features
We introduce algorithms to visualize feature spaces used by object detectors.
Object Detectors Emerge in Deep Scene CNNs
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.
ImageNet Large Scale Visual Recognition Challenge
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.
3D ShapeNets: A Deep Representation for Volumetric Shapes
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)
Looking Beyond the Visible Scene
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.
Large-Scale Video Summarization Using Web-Image Priors
Given the enormous growth in user-generated videos, it is becoming increasingly important to be able to navigate them efficiently.
Inverting and Visualizing Features for Object Detection
By visualizing feature spaces, we can gain a more intuitive understanding of our detection systems.
