8,799 research outputs found
Many Task Learning with Task Routing
Typical multi-task learning (MTL) methods rely on architectural adjustments
and a large trainable parameter set to jointly optimize over several tasks.
However, when the number of tasks increases so do the complexity of the
architectural adjustments and resource requirements. In this paper, we
introduce a method which applies a conditional feature-wise transformation over
the convolutional activations that enables a model to successfully perform a
large number of tasks. To distinguish from regular MTL, we introduce Many Task
Learning (MaTL) as a special case of MTL where more than 20 tasks are performed
by a single model. Our method dubbed Task Routing (TR) is encapsulated in a
layer we call the Task Routing Layer (TRL), which applied in an MaTL scenario
successfully fits hundreds of classification tasks in one model. We evaluate
our method on 5 datasets against strong baselines and state-of-the-art
approaches.Comment: 8 Pages, 5 Figures, 2 Table
CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing
We address the problem of cross-modal information retrieval in the domain of
remote sensing. In particular, we are interested in two application scenarios:
i) cross-modal retrieval between panchromatic (PAN) and multi-spectral imagery,
and ii) multi-label image retrieval between very high resolution (VHR) images
and speech based label annotations. Notice that these multi-modal retrieval
scenarios are more challenging than the traditional uni-modal retrieval
approaches given the inherent differences in distributions between the
modalities. However, with the growing availability of multi-source remote
sensing data and the scarcity of enough semantic annotations, the task of
multi-modal retrieval has recently become extremely important. In this regard,
we propose a novel deep neural network based architecture which is considered
to learn a discriminative shared feature space for all the input modalities,
suitable for semantically coherent information retrieval. Extensive experiments
are carried out on the benchmark large-scale PAN - multi-spectral DSRSID
dataset and the multi-label UC-Merced dataset. Together with the Merced
dataset, we generate a corpus of speech signals corresponding to the labels.
Superior performance with respect to the current state-of-the-art is observed
in all the cases
Attentive Single-Tasking of Multiple Tasks
In this work we address task interference in universal networks by
considering that a network is trained on multiple tasks, but performs one task
at a time, an approach we refer to as "single-tasking multiple tasks". The
network thus modifies its behaviour through task-dependent feature adaptation,
or task attention. This gives the network the ability to accentuate the
features that are adapted to a task, while shunning irrelevant ones. We further
reduce task interference by forcing the task gradients to be statistically
indistinguishable through adversarial training, ensuring that the common
backbone architecture serving all tasks is not dominated by any of the
task-specific gradients. Results in three multi-task dense labelling problems
consistently show: (i) a large reduction in the number of parameters while
preserving, or even improving performance and (ii) a smooth trade-off between
computation and multi-task accuracy. We provide our system's code and
pre-trained models at http://vision.ee.ethz.ch/~kmaninis/astmt/.Comment: CVPR 2019 Camera Read
MMFL-Net: Multi-scale and Multi-granularity Feature Learning for Cross-domain Fashion Retrieval
Instance-level image retrieval in fashion is a challenging issue owing to its
increasing importance in real-scenario visual fashion search. Cross-domain
fashion retrieval aims to match the unconstrained customer images as queries
for photographs provided by retailers; however, it is a difficult task due to a
wide range of consumer-to-shop (C2S) domain discrepancies and also considering
that clothing image is vulnerable to various non-rigid deformations. To this
end, we propose a novel multi-scale and multi-granularity feature learning
network (MMFL-Net), which can jointly learn global-local aggregation feature
representations of clothing images in a unified framework, aiming to train a
cross-domain model for C2S fashion visual similarity. First, a new
semantic-spatial feature fusion part is designed to bridge the semantic-spatial
gap by applying top-down and bottom-up bidirectional multi-scale feature
fusion. Next, a multi-branch deep network architecture is introduced to capture
global salient, part-informed, and local detailed information, and extracting
robust and discrimination feature embedding by integrating the similarity
learning of coarse-to-fine embedding with the multiple granularities. Finally,
the improved trihard loss, center loss, and multi-task classification loss are
adopted for our MMFL-Net, which can jointly optimize intra-class and
inter-class distance and thus explicitly improve intra-class compactness and
inter-class discriminability between its visual representations for feature
learning. Furthermore, our proposed model also combines the multi-task
attribute recognition and classification module with multi-label semantic
attributes and product ID labels. Experimental results demonstrate that our
proposed MMFL-Net achieves significant improvement over the state-of-the-art
methods on the two datasets, DeepFashion-C2S and Street2Shop.Comment: 27 pages, 12 figures, Published by <Multimedia Tools and
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