224 research outputs found
Visualizing Deep Networks by Optimizing with Integrated Gradients
Understanding and interpreting the decisions made by deep learning models is
valuable in many domains. In computer vision, computing heatmaps from a deep
network is a popular approach for visualizing and understanding deep networks.
However, heatmaps that do not correlate with the network may mislead human,
hence the performance of heatmaps in providing a faithful explanation to the
underlying deep network is crucial. In this paper, we propose I-GOS, which
optimizes for a heatmap so that the classification scores on the masked image
would maximally decrease. The main novelty of the approach is to compute
descent directions based on the integrated gradients instead of the normal
gradient, which avoids local optima and speeds up convergence. Compared with
previous approaches, our method can flexibly compute heatmaps at any resolution
for different user needs. Extensive experiments on several benchmark datasets
show that the heatmaps produced by our approach are more correlated with the
decision of the underlying deep network, in comparison with other
state-of-the-art approaches
GASP : Geometric Association with Surface Patches
A fundamental challenge to sensory processing tasks in perception and
robotics is the problem of obtaining data associations across views. We present
a robust solution for ascertaining potentially dense surface patch (superpixel)
associations, requiring just range information. Our approach involves
decomposition of a view into regularized surface patches. We represent them as
sequences expressing geometry invariantly over their superpixel neighborhoods,
as uniquely consistent partial orderings. We match these representations
through an optimal sequence comparison metric based on the Damerau-Levenshtein
distance - enabling robust association with quadratic complexity (in contrast
to hitherto employed joint matching formulations which are NP-complete). The
approach is able to perform under wide baselines, heavy rotations, partial
overlaps, significant occlusions and sensor noise.
The technique does not require any priors -- motion or otherwise, and does
not make restrictive assumptions on scene structure and sensor movement. It
does not require appearance -- is hence more widely applicable than appearance
reliant methods, and invulnerable to related ambiguities such as textureless or
aliased content. We present promising qualitative and quantitative results
under diverse settings, along with comparatives with popular approaches based
on range as well as RGB-D data.Comment: International Conference on 3D Vision, 201
Beyond Sentiment: The Manifold of Human Emotions
Sentiment analysis predicts the presence of positive or negative emotions in
a text document. In this paper we consider higher dimensional extensions of the
sentiment concept, which represent a richer set of human emotions. Our approach
goes beyond previous work in that our model contains a continuous manifold
rather than a finite set of human emotions. We investigate the resulting model,
compare it to psychological observations, and explore its predictive
capabilities. Besides obtaining significant improvements over a baseline
without manifold, we are also able to visualize different notions of positive
sentiment in different domains.Comment: 15 pages, 7 figure
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