Pixel Objectness
Suyog Dutt Jain, Bo Xiong, Kristen Grauman

TL;DR
This paper introduces a deep learning framework that generates pixel-level object segmentations for novel images, even for unseen categories, by combining weak and strong supervision, improving state-of-the-art results.
Contribution
It presents an end-to-end fully convolutional network that learns to produce foreground masks using limited boundary annotations and image-level labels, enabling generalization to unseen object categories.
Findings
Significantly outperforms previous methods on ImageNet and MIT datasets.
Successfully generalizes to over 1 million images with unseen categories.
Enhances image retrieval and retargeting tasks with high-quality foreground maps.
Abstract
We propose an end-to-end learning framework for generating foreground object segmentations. Given a single novel image, our approach produces pixel-level masks for all "object-like" regions---even for object categories never seen during training. We formulate the task as a structured prediction problem of assigning foreground/background labels to all pixels, implemented using a deep fully convolutional network. Key to our idea is training with a mix of image-level object category examples together with relatively few images with boundary-level annotations. Our method substantially improves the state-of-the-art on foreground segmentation for ImageNet and MIT Object Discovery datasets. Furthermore, on over 1 million images, we show that it generalizes well to segment object categories unseen in the foreground maps used for training. Finally, we demonstrate how our approach benefits image…
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Taxonomy
TopicsVisual perception and processing mechanisms · Visual Attention and Saliency Detection · Aesthetic Perception and Analysis
