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11/03/2022
Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks
There is limited understanding of the information captured by deep spati...
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06/06/2022
A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information
Deep spatiotemporal models are used in a variety of computer vision task...
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10/20/2021
Simpler Does It: Generating Semantic Labels with Objectness Guidance
Existing weakly or semi-supervised semantic segmentation methods utilize...
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08/23/2021
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness
In this paper, we present a strategy for training convolutional neural n...
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08/17/2021
Global Pooling, More than Meets the Eye: Position Information is Encoded Channel-Wise in CNNs
In this paper, we challenge the common assumption that collapsing the sp...
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01/28/2021
Position, Padding and Predictions: A Deeper Look at Position Information in CNNs
In contrast to fully connected networks, Convolutional Neural Networks (...
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01/27/2021
Shape or Texture: Understanding Discriminative Features in CNNs
Contrasting the previous evidence that neurons in the later layers of a ...
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08/13/2020
Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness
In this paper, we present a strategy for training convolutional neural n...
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08/23/2019
