6,370 research outputs found
FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation
Over the past few years, we have witnessed the success of deep learning in
image recognition thanks to the availability of large-scale human-annotated
datasets such as PASCAL VOC, ImageNet, and COCO. Although these datasets have
covered a wide range of object categories, there are still a significant number
of objects that are not included. Can we perform the same task without a lot of
human annotations? In this paper, we are interested in few-shot object
segmentation where the number of annotated training examples are limited to 5
only. To evaluate and validate the performance of our approach, we have built a
few-shot segmentation dataset, FSS-1000, which consists of 1000 object classes
with pixelwise annotation of ground-truth segmentation. Unique in FSS-1000, our
dataset contains significant number of objects that have never been seen or
annotated in previous datasets, such as tiny daily objects, merchandise,
cartoon characters, logos, etc. We build our baseline model using standard
backbone networks such as VGG-16, ResNet-101, and Inception. To our surprise,
we found that training our model from scratch using FSS-1000 achieves
comparable and even better results than training with weights pre-trained by
ImageNet which is more than 100 times larger than FSS-1000. Both our approach
and dataset are simple, effective, and easily extensible to learn segmentation
of new object classes given very few annotated training examples. Dataset is
available at https://github.com/HKUSTCV/FSS-1000
Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation
We propose an approach to discover class-specific pixels for the
weakly-supervised semantic segmentation task. We show that properly combining
saliency and attention maps allows us to obtain reliable cues capable of
significantly boosting the performance. First, we propose a simple yet powerful
hierarchical approach to discover the class-agnostic salient regions, obtained
using a salient object detector, which otherwise would be ignored. Second, we
use fully convolutional attention maps to reliably localize the class-specific
regions in a given image. We combine these two cues to discover class-specific
pixels which are then used as an approximate ground truth for training a CNN.
While solving the weakly supervised semantic segmentation task, we ensure that
the image-level classification task is also solved in order to enforce the CNN
to assign at least one pixel to each object present in the image.
Experimentally, on the PASCAL VOC12 val and test sets, we obtain the mIoU of
60.8% and 61.9%, achieving the performance gains of 5.1% and 5.2% compared to
the published state-of-the-art results. The code is made publicly available
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