6,715 research outputs found
Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning
Deep neural networks require a large amount of labeled training data during
supervised learning. However, collecting and labeling so much data might be
infeasible in many cases. In this paper, we introduce a source-target selective
joint fine-tuning scheme for improving the performance of deep learning tasks
with insufficient training data. In this scheme, a target learning task with
insufficient training data is carried out simultaneously with another source
learning task with abundant training data. However, the source learning task
does not use all existing training data. Our core idea is to identify and use a
subset of training images from the original source learning task whose
low-level characteristics are similar to those from the target learning task,
and jointly fine-tune shared convolutional layers for both tasks. Specifically,
we compute descriptors from linear or nonlinear filter bank responses on
training images from both tasks, and use such descriptors to search for a
desired subset of training samples for the source learning task.
Experiments demonstrate that our selective joint fine-tuning scheme achieves
state-of-the-art performance on multiple visual classification tasks with
insufficient training data for deep learning. Such tasks include Caltech 256,
MIT Indoor 67, Oxford Flowers 102 and Stanford Dogs 120. In comparison to
fine-tuning without a source domain, the proposed method can improve the
classification accuracy by 2% - 10% using a single model.Comment: To appear in 2017 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2017
Combining multiple resolutions into hierarchical representations for kernel-based image classification
Geographic object-based image analysis (GEOBIA) framework has gained
increasing interest recently. Following this popular paradigm, we propose a
novel multiscale classification approach operating on a hierarchical image
representation built from two images at different resolutions. They capture the
same scene with different sensors and are naturally fused together through the
hierarchical representation, where coarser levels are built from a Low Spatial
Resolution (LSR) or Medium Spatial Resolution (MSR) image while finer levels
are generated from a High Spatial Resolution (HSR) or Very High Spatial
Resolution (VHSR) image. Such a representation allows one to benefit from the
context information thanks to the coarser levels, and subregions spatial
arrangement information thanks to the finer levels. Two dedicated structured
kernels are then used to perform machine learning directly on the constructed
hierarchical representation. This strategy overcomes the limits of conventional
GEOBIA classification procedures that can handle only one or very few
pre-selected scales. Experiments run on an urban classification task show that
the proposed approach can highly improve the classification accuracy w.r.t.
conventional approaches working on a single scale.Comment: International Conference on Geographic Object-Based Image Analysis
(GEOBIA 2016), University of Twente in Enschede, The Netherland
Texture Segregation By Visual Cortex: Perceptual Grouping, Attention, and Learning
A neural model is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model brings together five interacting processes: region-based texture classification, contour-based boundary grouping, surface filling-in, spatial attention, and object attention. The model shows how form boundaries can determine regions in which surface filling-in occurs; how surface filling-in interacts with spatial attention to generate a form-fitting distribution of spatial attention, or attentional shroud; how the strongest shroud can inhibit weaker shrouds; and how the winning shroud regulates learning of texture categories, and thus the allocation of object attention. The model can discriminate abutted textures with blurred boundaries and is sensitive to texture boundary attributes like discontinuities in orientation and texture flow curvature as well as to relative orientations of texture elements. The model quantitatively fits a large set of human psychophysical data on orientation-based textures. Object boundar output of the model is compared to computer vision algorithms using a set of human segmented photographic images. The model classifies textures and suppresses noise using a multiple scale oriented filterbank and a distributed Adaptive Resonance Theory (dART) classifier. The matched signal between the bottom-up texture inputs and top-down learned texture categories is utilized by oriented competitive and cooperative grouping processes to generate texture boundaries that control surface filling-in and spatial attention. Topdown modulatory attentional feedback from boundary and surface representations to early filtering stages results in enhanced texture boundaries and more efficient learning of texture within attended surface regions. Surface-based attention also provides a self-supervising training signal for learning new textures. Importance of the surface-based attentional feedback in texture learning and classification is tested using a set of textured images from the Brodatz micro-texture album. Benchmark studies vary from 95.1% to 98.6% with attention, and from 90.6% to 93.2% without attention.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-0423); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624
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