222 research outputs found
Colorization as a Proxy Task for Visual Understanding
We investigate and improve self-supervision as a drop-in replacement for
ImageNet pretraining, focusing on automatic colorization as the proxy task.
Self-supervised training has been shown to be more promising for utilizing
unlabeled data than other, traditional unsupervised learning methods. We build
on this success and evaluate the ability of our self-supervised network in
several contexts. On VOC segmentation and classification tasks, we present
results that are state-of-the-art among methods not using ImageNet labels for
pretraining representations.
Moreover, we present the first in-depth analysis of self-supervision via
colorization, concluding that formulation of the loss, training details and
network architecture play important roles in its effectiveness. This
investigation is further expanded by revisiting the ImageNet pretraining
paradigm, asking questions such as: How much training data is needed? How many
labels are needed? How much do features change when fine-tuned? We relate these
questions back to self-supervision by showing that colorization provides a
similarly powerful supervisory signal as various flavors of ImageNet
pretraining.Comment: CVPR 2017 (Project page:
http://people.cs.uchicago.edu/~larsson/color-proxy/
Multi-task Self-Supervised Visual Learning
We investigate methods for combining multiple self-supervised tasks--i.e.,
supervised tasks where data can be collected without manual labeling--in order
to train a single visual representation. First, we provide an apples-to-apples
comparison of four different self-supervised tasks using the very deep
ResNet-101 architecture. We then combine tasks to jointly train a network. We
also explore lasso regularization to encourage the network to factorize the
information in its representation, and methods for "harmonizing" network inputs
in order to learn a more unified representation. We evaluate all methods on
ImageNet classification, PASCAL VOC detection, and NYU depth prediction. Our
results show that deeper networks work better, and that combining tasks--even
via a naive multi-head architecture--always improves performance. Our best
joint network nearly matches the PASCAL performance of a model pre-trained on
ImageNet classification, and matches the ImageNet network on NYU depth
prediction.Comment: Published at ICCV 201
PCCNet:A Few-Shot Patch-wise Contrastive Colorization Network
Few-shot colorization aims to learn a model to colorize images with little training data. Yet, existing models often fail to keep color consistency due to ignored patch correlations of the images. In this paper, we propose PCCNet, a novel Patch-wise Contrastive Colorization Network to learn color synthesis by measuring the similarities and variations of image patches in two different aspects: inter-image and intra-image. Specifically, for inter-image, we investigate a patch-wise contrastive learning mechanism with positive and negative samples constraint to distinguish color features between patches across images. For intra-image, we explore a new intra-image correlation loss function to measure the similarity distribution which reveals structural relations between patches within an image. Furthermore, we propose a novel color memory loss that improves the accuracy of the memory module to store and retrieve data. Experiments show that our method allows the correctly saturated color to spread naturally over objects and also achieves higher scores in quantitative comparisons with related methods
PCCNet:A Few-Shot Patch-wise Contrastive Colorization Network
Few-shot colorization aims to learn a model to colorize images with little training data. Yet, existing models often fail to keep color consistency due to ignored patch correlations of the images. In this paper, we propose PCCNet, a novel Patch-wise Contrastive Colorization Network to learn color synthesis by measuring the similarities and variations of image patches in two different aspects: inter-image and intra-image. Specifically, for inter-image, we investigate a patch-wise contrastive learning mechanism with positive and negative samples constraint to distinguish color features between patches across images. For intra-image, we explore a new intra-image correlation loss function to measure the similarity distribution which reveals structural relations between patches within an image. Furthermore, we propose a novel color memory loss that improves the accuracy of the memory module to store and retrieve data. Experiments show that our method allows the correctly saturated color to spread naturally over objects and also achieves higher scores in quantitative comparisons with related methods
Deep Video Color Propagation
Traditional approaches for color propagation in videos rely on some form of
matching between consecutive video frames. Using appearance descriptors, colors
are then propagated both spatially and temporally. These methods, however, are
computationally expensive and do not take advantage of semantic information of
the scene. In this work we propose a deep learning framework for color
propagation that combines a local strategy, to propagate colors frame-by-frame
ensuring temporal stability, and a global strategy, using semantics for color
propagation within a longer range. Our evaluation shows the superiority of our
strategy over existing video and image color propagation methods as well as
neural photo-realistic style transfer approaches.Comment: BMVC 201
PCCNet:A Few-Shot Patch-wise Contrastive Colorization Network
Few-shot colorization aims to learn a model to colorize images with little training data. Yet, existing models often fail to keep color consistency due to ignored patch correlations of the images. In this paper, we propose PCCNet, a novel Patch-wise Contrastive Colorization Network to learn color synthesis by measuring the similarities and variations of image patches in two different aspects: inter-image and intra-image. Specifically, for inter-image, we investigate a patch-wise contrastive learning mechanism with positive and negative samples constraint to distinguish color features between patches across images. For intra-image, we explore a new intra-image correlation loss function to measure the similarity distribution which reveals structural relations between patches within an image. Furthermore, we propose a novel color memory loss that improves the accuracy of the memory module to store and retrieve data. Experiments show that our method allows the correctly saturated color to spread naturally over objects and also achieves higher scores in quantitative comparisons with related methods
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