5,806 research outputs found
Modeling Camera Effects to Improve Visual Learning from Synthetic Data
Recent work has focused on generating synthetic imagery to increase the size
and variability of training data for learning visual tasks in urban scenes.
This includes increasing the occurrence of occlusions or varying environmental
and weather effects. However, few have addressed modeling variation in the
sensor domain. Sensor effects can degrade real images, limiting
generalizability of network performance on visual tasks trained on synthetic
data and tested in real environments. This paper proposes an efficient,
automatic, physically-based augmentation pipeline to vary sensor effects
--chromatic aberration, blur, exposure, noise, and color cast-- for synthetic
imagery. In particular, this paper illustrates that augmenting synthetic
training datasets with the proposed pipeline reduces the domain gap between
synthetic and real domains for the task of object detection in urban driving
scenes
Learning to Attend, Copy, and Generate for Session-Based Query Suggestion
Users try to articulate their complex information needs during search
sessions by reformulating their queries. To make this process more effective,
search engines provide related queries to help users in specifying the
information need in their search process. In this paper, we propose a
customized sequence-to-sequence model for session-based query suggestion. In
our model, we employ a query-aware attention mechanism to capture the structure
of the session context. is enables us to control the scope of the session from
which we infer the suggested next query, which helps not only handle the noisy
data but also automatically detect session boundaries. Furthermore, we observe
that, based on the user query reformulation behavior, within a single session a
large portion of query terms is retained from the previously submitted queries
and consists of mostly infrequent or unseen terms that are usually not included
in the vocabulary. We therefore empower the decoder of our model to access the
source words from the session context during decoding by incorporating a copy
mechanism. Moreover, we propose evaluation metrics to assess the quality of the
generative models for query suggestion. We conduct an extensive set of
experiments and analysis. e results suggest that our model outperforms the
baselines both in terms of the generating queries and scoring candidate queries
for the task of query suggestion.Comment: Accepted to be published at The 26th ACM International Conference on
Information and Knowledge Management (CIKM2017
Deep Convolutional Ranking for Multilabel Image Annotation
Multilabel image annotation is one of the most important challenges in
computer vision with many real-world applications. While existing work usually
use conventional visual features for multilabel annotation, features based on
Deep Neural Networks have shown potential to significantly boost performance.
In this work, we propose to leverage the advantage of such features and analyze
key components that lead to better performances. Specifically, we show that a
significant performance gain could be obtained by combining convolutional
architectures with approximate top- ranking objectives, as thye naturally
fit the multilabel tagging problem. Our experiments on the NUS-WIDE dataset
outperforms the conventional visual features by about 10%, obtaining the best
reported performance in the literature
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