40,067 research outputs found
FiLM: Visual Reasoning with a General Conditioning Layer
We introduce a general-purpose conditioning method for neural networks called
FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network
computation via a simple, feature-wise affine transformation based on
conditioning information. We show that FiLM layers are highly effective for
visual reasoning - answering image-related questions which require a
multi-step, high-level process - a task which has proven difficult for standard
deep learning methods that do not explicitly model reasoning. Specifically, we
show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error
for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are
robust to ablations and architectural modifications, and 4) generalize well to
challenging, new data from few examples or even zero-shot.Comment: AAAI 2018. Code available at http://github.com/ethanjperez/film .
Extends arXiv:1707.0301
Cross-convolutional-layer Pooling for Image Recognition
Recent studies have shown that a Deep Convolutional Neural Network (DCNN)
pretrained on a large image dataset can be used as a universal image
descriptor, and that doing so leads to impressive performance for a variety of
image classification tasks. Most of these studies adopt activations from a
single DCNN layer, usually the fully-connected layer, as the image
representation. In this paper, we proposed a novel way to extract image
representations from two consecutive convolutional layers: one layer is
utilized for local feature extraction and the other serves as guidance to pool
the extracted features. By taking different viewpoints of convolutional layers,
we further develop two schemes to realize this idea. The first one directly
uses convolutional layers from a DCNN. The second one applies the pretrained
CNN on densely sampled image regions and treats the fully-connected activations
of each image region as convolutional feature activations. We then train
another convolutional layer on top of that as the pooling-guidance
convolutional layer. By applying our method to three popular visual
classification tasks, we find our first scheme tends to perform better on the
applications which need strong discrimination on subtle object patterns within
small regions while the latter excels in the cases that require discrimination
on category-level patterns. Overall, the proposed method achieves superior
performance over existing ways of extracting image representations from a DCNN.Comment: Fixed typos. Journal extension of arXiv:1411.7466. Accepted to IEEE
Transactions on Pattern Analysis and Machine Intelligenc
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