43,094 research outputs found
Object detection via a multi-region & semantic segmentation-aware CNN model
We propose an object detection system that relies on a multi-region deep
convolutional neural network (CNN) that also encodes semantic
segmentation-aware features. The resulting CNN-based representation aims at
capturing a diverse set of discriminative appearance factors and exhibits
localization sensitivity that is essential for accurate object localization. We
exploit the above properties of our recognition module by integrating it on an
iterative localization mechanism that alternates between scoring a box proposal
and refining its location with a deep CNN regression model. Thanks to the
efficient use of our modules, we detect objects with very high localization
accuracy. On the detection challenges of PASCAL VOC2007 and PASCAL VOC2012 we
achieve mAP of 78.2% and 73.9% correspondingly, surpassing any other published
work by a significant margin.Comment: Extended technical report -- short version to appear at ICCV 201
Learning Video Object Segmentation with Visual Memory
This paper addresses the task of segmenting moving objects in unconstrained
videos. We introduce a novel two-stream neural network with an explicit memory
module to achieve this. The two streams of the network encode spatial and
temporal features in a video sequence respectively, while the memory module
captures the evolution of objects over time. The module to build a "visual
memory" in video, i.e., a joint representation of all the video frames, is
realized with a convolutional recurrent unit learned from a small number of
training video sequences. Given a video frame as input, our approach assigns
each pixel an object or background label based on the learned spatio-temporal
features as well as the "visual memory" specific to the video, acquired
automatically without any manually-annotated frames. The visual memory is
implemented with convolutional gated recurrent units, which allows to propagate
spatial information over time. We evaluate our method extensively on two
benchmarks, DAVIS and Freiburg-Berkeley motion segmentation datasets, and show
state-of-the-art results. For example, our approach outperforms the top method
on the DAVIS dataset by nearly 6%. We also provide an extensive ablative
analysis to investigate the influence of each component in the proposed
framework
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