3 research outputs found
Deep Learning for Object Saliency Detection and Image Segmentation
In this paper, we propose several novel deep learning methods for object
saliency detection based on the powerful convolutional neural networks. In our
approach, we use a gradient descent method to iteratively modify an input image
based on the pixel-wise gradients to reduce a cost function measuring the
class-specific objectness of the image. The pixel-wise gradients can be
efficiently computed using the back-propagation algorithm. The discrepancy
between the modified image and the original one may be used as a saliency map
for the image. Moreover, we have further proposed several new training methods
to learn saliency-specific convolutional nets for object saliency detection, in
order to leverage the available pixel-wise segmentation information. Our
methods are extremely computationally efficient (processing 20-40 images per
second in one GPU). In this work, we use the computed saliency maps for image
segmentation. Experimental results on two benchmark tasks, namely Microsoft
COCO and Pascal VOC 2012, have shown that our proposed methods can generate
high-quality salience maps, clearly outperforming many existing methods. In
particular, our approaches excel in handling many difficult images, which
contain complex background, highly-variable salient objects, multiple objects,
and/or very small salient objects.Comment: 9 pages, 126 figures, technical repor
Suction Grasp Region Prediction using Self-supervised Learning for Object Picking in Dense Clutter
This paper focuses on robotic picking tasks in cluttered scenario. Because of
the diversity of poses, types of stack and complicated background in bin
picking situation, it is much difficult to recognize and estimate their pose
before grasping them. Here, this paper combines Resnet with U-net structure, a
special framework of Convolution Neural Networks (CNN), to predict picking
region without recognition and pose estimation. And it makes robotic picking
system learn picking skills from scratch. At the same time, we train the
network end to end with online samples. In the end of this paper, several
experiments are conducted to demonstrate the performance of our methods.Comment: 6 pages, 7 figures, conferenc
Automatic Attribute Discovery with Neural Activations
How can a machine learn to recognize visual attributes emerging out of online
community without a definitive supervised dataset? This paper proposes an
automatic approach to discover and analyze visual attributes from a noisy
collection of image-text data on the Web. Our approach is based on the
relationship between attributes and neural activations in the deep network. We
characterize the visual property of the attribute word as a divergence within
weakly-annotated set of images. We show that the neural activations are useful
for discovering and learning a classifier that well agrees with human
perception from the noisy real-world Web data. The empirical study suggests the
layered structure of the deep neural networks also gives us insights into the
perceptual depth of the given word. Finally, we demonstrate that we can utilize
highly-activating neurons for finding semantically relevant regions.Comment: ECCV 201