4,990 research outputs found
Fine-grained classification via mixture of deep convolutional neural networks
We present a novel deep convolutional neural network (DCNN) system for fine-grained image classification, called a mixture of DCNNs (MixDCNN). The fine-grained image classification problem is characterised by large intra-class variations and small inter-class variations. To overcome these problems our proposed MixDCNN system partitions images into K subsets of similar images and learns an expert DCNN for each subset. The output from each of the K DCNNs is combined to form a single classification decision. In contrast to previous techniques, we provide a formulation to perform joint end-to-end training of the K DCNNs simultaneously. Extensive experiments, on three datasets using two network structures (AlexNet and GoogLeNet), show that the proposed MixDCNN system consistently outperforms other methods. It provides a relative improvement of 12.7% and achieves state-of-the-art results on two dataset
Part Detector Discovery in Deep Convolutional Neural Networks
Current fine-grained classification approaches often rely on a robust
localization of object parts to extract localized feature representations
suitable for discrimination. However, part localization is a challenging task
due to the large variation of appearance and pose. In this paper, we show how
pre-trained convolutional neural networks can be used for robust and efficient
object part discovery and localization without the necessity to actually train
the network on the current dataset. Our approach called "part detector
discovery" (PDD) is based on analyzing the gradient maps of the network outputs
and finding activation centers spatially related to annotated semantic parts or
bounding boxes.
This allows us not just to obtain excellent performance on the CUB200-2011
dataset, but in contrast to previous approaches also to perform detection and
bird classification jointly without requiring a given bounding box annotation
during testing and ground-truth parts during training. The code is available at
http://www.inf-cv.uni-jena.de/part_discovery and
https://github.com/cvjena/PartDetectorDisovery.Comment: Accepted for publication on Asian Conference on Computer Vision
(ACCV) 201
GRASS: Generative Recursive Autoencoders for Shape Structures
We introduce a novel neural network architecture for encoding and synthesis
of 3D shapes, particularly their structures. Our key insight is that 3D shapes
are effectively characterized by their hierarchical organization of parts,
which reflects fundamental intra-shape relationships such as adjacency and
symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a
flat, unlabeled, arbitrary part layout to a compact code. The code effectively
captures hierarchical structures of man-made 3D objects of varying structural
complexities despite being fixed-dimensional: an associated decoder maps a code
back to a full hierarchy. The learned bidirectional mapping is further tuned
using an adversarial setup to yield a generative model of plausible structures,
from which novel structures can be sampled. Finally, our structure synthesis
framework is augmented by a second trained module that produces fine-grained
part geometry, conditioned on global and local structural context, leading to a
full generative pipeline for 3D shapes. We demonstrate that without
supervision, our network learns meaningful structural hierarchies adhering to
perceptual grouping principles, produces compact codes which enable
applications such as shape classification and partial matching, and supports
shape synthesis and interpolation with significant variations in topology and
geometry.Comment: Corresponding author: Kai Xu ([email protected]
Compositional Model based Fisher Vector Coding for Image Classification
Deriving from the gradient vector of a generative model of local features,
Fisher vector coding (FVC) has been identified as an effective coding method
for image classification. Most, if not all, FVC implementations employ the
Gaussian mixture model (GMM) to depict the generation process of local
features. However, the representative power of the GMM could be limited because
it essentially assumes that local features can be characterized by a fixed
number of feature prototypes and the number of prototypes is usually small in
FVC. To handle this limitation, in this paper we break the convention which
assumes that a local feature is drawn from one of few Gaussian distributions.
Instead, we adopt a compositional mechanism which assumes that a local feature
is drawn from a Gaussian distribution whose mean vector is composed as the
linear combination of multiple key components and the combination weight is a
latent random variable. In this way, we can greatly enhance the representative
power of the generative model of FVC. To implement our idea, we designed two
particular generative models with such a compositional mechanism.Comment: Fixed typos. 16 pages. Appearing in IEEE T. Pattern Analysis and
Machine Intelligence (TPAMI
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