[2005.10825] Instance-aware Image Colorizationopen searchopen navigation menucontact arXivsubscribe to arXiv mailings

Image colorization is inherently an ill-posed problem with multi-modal uncertainty. Previous methods leverage the deep neural network to map input grayscale images to plausible color outputs directly. Although these learning-based methods have shown impressive performance, they usually fail on the input images that contain multiple objects. The leading cause is that existing models perform learning and colorization on the entire image. In the absence of a clear figure-ground separation, these models cannot effectively locate and learn meaningful object-level semantics. In this paper, we propose a method for achieving instance-aware colorization. Our network architecture leverages an off-the-shelf object detector to obtain cropped object images and uses an instance colorization network to extract object-level features. We use a similar network to extract the full-image features and apply a fusion module to full object-level and image-level features to predict the final colors. Both colo

2 mentions: @AkiraTOSEI@AkiraTOSEI
Date: 2020/11/19 11:21

Referring Tweets

@AkiraTOSEI t.co/83v2yjc38z 白黒写真のカラー化において、画像全体を直接カラー化するより個々の物体をカラー化する方が簡単なタスクであるという見地から、物体を学習済みモデルで切り出したあとに処理することでカラー化する。背景に引きずれられずに物体毎に塗り分けができる t.co/iRmhgDQ1SX
@AkiraTOSEI t.co/83v2yjc38z In the colorization, objects are colored by cropping and processing them in the view that it is easier to colorize individual objects than to colorize the whole image directly. Each object can be colored without being influenced by backgrounds. t.co/AXJw5BMIiw

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