34,824 research outputs found
Semantic Visual Localization
Robust visual localization under a wide range of viewing conditions is a
fundamental problem in computer vision. Handling the difficult cases of this
problem is not only very challenging but also of high practical relevance,
e.g., in the context of life-long localization for augmented reality or
autonomous robots. In this paper, we propose a novel approach based on a joint
3D geometric and semantic understanding of the world, enabling it to succeed
under conditions where previous approaches failed. Our method leverages a novel
generative model for descriptor learning, trained on semantic scene completion
as an auxiliary task. The resulting 3D descriptors are robust to missing
observations by encoding high-level 3D geometric and semantic information.
Experiments on several challenging large-scale localization datasets
demonstrate reliable localization under extreme viewpoint, illumination, and
geometry changes
The effectiveness of three types of practice in word recognition in grades II and III
Thesis (Ed.M.)--Boston Universit
Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks
An important goal of computer vision is to build systems that learn visual
representations over time that can be applied to many tasks. In this paper, we
investigate a vision-language embedding as a core representation and show that
it leads to better cross-task transfer than standard multi-task learning. In
particular, the task of visual recognition is aligned to the task of visual
question answering by forcing each to use the same word-region embeddings. We
show this leads to greater inductive transfer from recognition to VQA than
standard multitask learning. Visual recognition also improves, especially for
categories that have relatively few recognition training labels but appear
often in the VQA setting. Thus, our paper takes a small step towards creating
more general vision systems by showing the benefit of interpretable, flexible,
and trainable core representations.Comment: Accepted in ICCV 2017. The arxiv version has an extra analysis on
correlation with human attentio
Distinctive-attribute Extraction for Image Captioning
Image captioning, an open research issue, has been evolved with the progress
of deep neural networks. Convolutional neural networks (CNNs) and recurrent
neural networks (RNNs) are employed to compute image features and generate
natural language descriptions in the research. In previous works, a caption
involving semantic description can be generated by applying additional
information into the RNNs. In this approach, we propose a distinctive-attribute
extraction (DaE) which explicitly encourages significant meanings to generate
an accurate caption describing the overall meaning of the image with their
unique situation. Specifically, the captions of training images are analyzed by
term frequency-inverse document frequency (TF-IDF), and the analyzed semantic
information is trained to extract distinctive-attributes for inferring
captions. The proposed scheme is evaluated on a challenge data, and it improves
an objective performance while describing images in more detail.Comment: 14 main pages, 4 supplementary page
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Visual localization enables autonomous vehicles to navigate in their
surroundings and augmented reality applications to link virtual to real worlds.
Practical visual localization approaches need to be robust to a wide variety of
viewing condition, including day-night changes, as well as weather and seasonal
variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera
pose estimates. In this paper, we introduce the first benchmark datasets
specifically designed for analyzing the impact of such factors on visual
localization. Using carefully created ground truth poses for query images taken
under a wide variety of conditions, we evaluate the impact of various factors
on 6DOF camera pose estimation accuracy through extensive experiments with
state-of-the-art localization approaches. Based on our results, we draw
conclusions about the difficulty of different conditions, showing that
long-term localization is far from solved, and propose promising avenues for
future work, including sequence-based localization approaches and the need for
better local features. Our benchmark is available at visuallocalization.net.Comment: Accepted to CVPR 2018 as a spotligh
Development and evaluation of lessons for class and group situations in grade I. Volume I.
Thesis (Ed.M.)--Boston University
For volume II, please see: http://hdl.handle.net/2144/1415
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