27 research outputs found
Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
In this work, we propose an efficient and effective approach for
unconstrained salient object detection in images using deep convolutional
neural networks. Instead of generating thousands of candidate bounding boxes
and refining them, our network directly learns to generate the saliency map
containing the exact number of salient objects. During training, we convert the
ground-truth rectangular boxes to Gaussian distributions that better capture
the ROI regarding individual salient objects. During inference, the network
predicts Gaussian distributions centered at salient objects with an appropriate
covariance, from which bounding boxes are easily inferred. Notably, our network
performs saliency map prediction without pixel-level annotations, salient
object detection without object proposals, and salient object subitizing
simultaneously, all in a single pass within a unified framework. Extensive
experiments show that our approach outperforms existing methods on various
datasets by a large margin, and achieves more than 100 fps with VGG16 network
on a single GPU during inference
Large Scale Visual Recommendations From Street Fashion Images
We describe a completely automated large scale visual recommendation system
for fashion. Our focus is to efficiently harness the availability of large
quantities of online fashion images and their rich meta-data. Specifically, we
propose four data driven models in the form of Complementary Nearest Neighbor
Consensus, Gaussian Mixture Models, Texture Agnostic Retrieval and Markov Chain
LDA for solving this problem. We analyze relative merits and pitfalls of these
algorithms through extensive experimentation on a large-scale data set and
baseline them against existing ideas from color science. We also illustrate key
fashion insights learned through these experiments and show how they can be
employed to design better recommendation systems. Finally, we also outline a
large-scale annotated data set of fashion images (Fashion-136K) that can be
exploited for future vision research