Christopher Kanan

Rochester Institute of Technology, Cornell Tech, Paige
0000-0002-6412-995X
https://chriskanan.com
Publications74
h-index20
Citations2,227
Highly Influential Citations177
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  • Publications
  • Influence
Continual Lifelong Learning with Neural Networks: A Review
Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is mediated by a… Expand
  • 304
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Robotic grasp detection using deep convolutional neural networks
Deep learning has significantly advanced computer vision and natural language processing. While there have been some successes in robotics using deep learning, it has not been widely adopted. In this… Expand
  • 116
  • 20
SUN: Top-down saliency using natural statistics
When people try to find particular objects in natural scenes they make extensive use of knowledge about how and where objects tend to appear in a scene. Although many forms of such “top-down”… Expand
  • 235
  • 14
Color-to-Grayscale: Does the Method Matter in Image Recognition?
In image recognition it is often assumed the method used to convert color images to grayscale has little impact on recognition performance. We compare thirteen different grayscale algorithms with… Expand
  • 170
  • 14
An Analysis of Visual Question Answering Algorithms
In visual question answering (VQA), an algorithm must answer text-based questions about images. While multiple datasets for VQA have been created since late 2014, they all have flaws in both their… Expand
  • 88
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FearNet: Brain-Inspired Model for Incremental Learning
Incremental class learning involves sequentially learning classes in bursts of examples from the same class. This violates the assumptions that underlie methods for training standard deep neural… Expand
  • 126
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Robust classification of objects, faces, and flowers using natural image statistics
Classification of images in many category datasbets has rapidly improved in recent years. However, systems that perform well on particular datasets typically have one or more limitations such as a… Expand
  • 178
  • 10
Measuring Catastrophic Forgetting in Neural Networks
Deep neural networks are used in many state-of-the-art systems for machine perception. Once a network is trained to do a specific task, e.g., bird classification, it cannot easily be trained to do… Expand
  • 141
  • 10
Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning
Abstract Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on many computer vision tasks (e.g., object recognition, object detection, semantic… Expand
  • 89
  • 9
DVQA: Understanding Data Visualizations via Question Answering
Bar charts are an effective way to convey numeric information, but today's algorithms cannot parse them. Existing methods fail when faced with even minor variations in appearance. Here, we present… Expand
  • 35
  • 8